{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MhoQ0WE77laV"
   },
   "source": [
    "##### Copyright 2018 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2023-11-08T00:31:57.582337Z",
     "iopub.status.busy": "2023-11-08T00:31:57.582061Z",
     "iopub.status.idle": "2023-11-08T00:31:57.586462Z",
     "shell.execute_reply": "2023-11-08T00:31:57.585790Z"
    },
    "id": "_ckMIh7O7s6D"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2023-11-08T00:31:57.589670Z",
     "iopub.status.busy": "2023-11-08T00:31:57.589429Z",
     "iopub.status.idle": "2023-11-08T00:31:57.593266Z",
     "shell.execute_reply": "2023-11-08T00:31:57.592623Z"
    },
    "id": "vasWnqRgy1H4"
   },
   "outputs": [],
   "source": [
    "#@title MIT License\n",
    "#\n",
    "# Copyright (c) 2017 François Chollet\n",
    "#\n",
    "# Permission is hereby granted, free of charge, to any person obtaining a\n",
    "# copy of this software and associated documentation files (the \"Software\"),\n",
    "# to deal in the Software without restriction, including without limitation\n",
    "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
    "# and/or sell copies of the Software, and to permit persons to whom the\n",
    "# Software is furnished to do so, subject to the following conditions:\n",
    "#\n",
    "# The above copyright notice and this permission notice shall be included in\n",
    "# all copies or substantial portions of the Software.\n",
    "#\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
    "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
    "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
    "# DEALINGS IN THE SOFTWARE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jYysdyb-CaWM"
   },
   "source": [
    "# 基本分类：对服装图像进行分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S5Uhzt6vVIB2"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td><a target=\"_blank\" href=\"https://tensorflow.google.cn/tutorials/keras/classification\"><img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\">在 TensorFlow.org 上查看</a></td>\n",
    "  <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/keras/classification.ipynb\"><img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\">在 Google Colab 中运行</a></td>\n",
    "  <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/keras/classification.ipynb\"><img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\">在 GitHub 上查看源代码</a></td>\n",
    "  <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/zh-cn/tutorials/keras/classification.ipynb\"><img src=\"https://tensorflow.google.cn/images/download_logo_32px.png\">下载笔记本</a></td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FbVhjPpzn6BM"
   },
   "source": [
    "本指南将训练一个神经网络模型，对运动鞋和衬衫等服装图像进行分类。即使您不理解所有细节也没关系；这只是对完整 TensorFlow 程序的快速概述，详细内容会在您实际操作的同时进行介绍。\n",
    "\n",
    "本指南使用了 [tf.keras](https://tensorflow.google.cn/guide/keras)，它是 TensorFlow 中用来构建和训练模型的高级 API。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:31:57.596699Z",
     "iopub.status.busy": "2023-11-08T00:31:57.596464Z",
     "iopub.status.idle": "2023-11-08T00:32:00.493416Z",
     "shell.execute_reply": "2023-11-08T00:32:00.492593Z"
    },
    "id": "dzLKpmZICaWN"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-08 00:31:58.058238: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2023-11-08 00:31:58.058290: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2023-11-08 00:31:58.059927: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.15.0-rc1\n"
     ]
    }
   ],
   "source": [
    "# TensorFlow and tf.keras\n",
    "import tensorflow as tf\n",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yR0EdgrLCaWR"
   },
   "source": [
    "## 导入 Fashion MNIST 数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DLdCchMdCaWQ"
   },
   "source": [
    "本指南使用 [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) 数据集，该数据集包含 10 个类别的 70,000 个灰度图像。这些图像以低分辨率（28x28 像素）展示了单件衣物，如下所示：\n",
    "\n",
    "<table>\n",
    "  <tr><td>     <img alt=\"Fashion MNIST sprite\" src=\"https://tensorflow.google.cn/images/fashion-mnist-sprite.png\"> </td></tr>\n",
    "  <tr><td align=\"center\">     <b>图 1.</b>  <a href=\"https://github.com/zalandoresearch/fashion-mnist\">Fashion-MNIST 样本</a>（由 Zalando 提供，MIT 许可）。<br> </td></tr>\n",
    "</table>\n",
    "\n",
    "Fashion MNIST 旨在临时替代经典 [MNIST](http://yann.lecun.com/exdb/mnist/) 数据集，后者常被用作计算机视觉机器学习程序的“Hello, World”。MNIST 数据集包含手写数字（0、1、2 等）的图像，其格式与您将使用的衣物图像的格式相同。\n",
    "\n",
    "本指南使用 Fashion MNIST 来实现多样化，因为它比常规 MNIST 更具挑战性。这两个数据集都相对较小，都用于验证某个算法是否按预期工作。对于代码的测试和调试，它们都是很好的起点。\n",
    "\n",
    "在本指南中，我们使用 60,000 张图像来训练网络，使用 10,000 张图像来评估网络学习对图像进行分类的准确程度。您可以直接从 TensorFlow 中访问 Fashion MNIST。直接从 TensorFlow 中导入和加载 Fashion MNIST 数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:00.497855Z",
     "iopub.status.busy": "2023-11-08T00:32:00.496948Z",
     "iopub.status.idle": "2023-11-08T00:32:00.961685Z",
     "shell.execute_reply": "2023-11-08T00:32:00.960688Z"
    },
    "id": "7MqDQO0KCaWS"
   },
   "outputs": [],
   "source": [
    "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t9FDsUlxCaWW"
   },
   "source": [
    "加载数据集会返回四个 NumPy 数组：\n",
    "\n",
    "- `train_images` 和 `train_labels` 数组是*训练集*，即模型用于学习的数据。\n",
    "- *测试集*、`test_images` 和 `test_labels` 数组会被用来对模型进行测试。\n",
    "\n",
    "图像是 28x28 的 NumPy 数组，像素值介于 0 到 255 之间。*标签*是整数数组，介于 0 到 9 之间。这些标签对应于图像所代表的服装*类*：\n",
    "\n",
    "<table>\n",
    "  <tr>\n",
    "    <th>标签</th>\n",
    "    <th>类</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>0</td>\n",
    "    <td>T恤/上衣</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>1</td>\n",
    "    <td>裤子</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>2</td>\n",
    "    <td>套头衫</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>3</td>\n",
    "    <td>连衣裙</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>4</td>\n",
    "    <td>外套</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>5</td>\n",
    "    <td>凉鞋</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>6</td>\n",
    "    <td>衬衫</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>7</td>\n",
    "    <td>运动鞋</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>8</td>\n",
    "    <td>包</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>9</td>\n",
    "    <td>短靴</td>\n",
    "  </tr>\n",
    "</table>\n",
    "\n",
    "每个图像都会被映射到一个标签。由于数据集不包括*类名称*，请将它们存储在下方，供稍后绘制图像时使用："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:00.966946Z",
     "iopub.status.busy": "2023-11-08T00:32:00.966224Z",
     "iopub.status.idle": "2023-11-08T00:32:00.970181Z",
     "shell.execute_reply": "2023-11-08T00:32:00.969466Z"
    },
    "id": "IjnLH5S2CaWx"
   },
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Brm0b_KACaWX"
   },
   "source": [
    "## 浏览数据\n",
    "\n",
    "在训练模型之前，我们先浏览一下数据集的格式。以下代码显示训练集中有 60,000 个图像，每个图像由 28 x 28 的像素表示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:00.973607Z",
     "iopub.status.busy": "2023-11-08T00:32:00.973340Z",
     "iopub.status.idle": "2023-11-08T00:32:00.980293Z",
     "shell.execute_reply": "2023-11-08T00:32:00.979620Z"
    },
    "id": "zW5k_xz1CaWX"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cIAcvQqMCaWf"
   },
   "source": [
    "同样，训练集中有 60,000 个标签："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:00.983756Z",
     "iopub.status.busy": "2023-11-08T00:32:00.983484Z",
     "iopub.status.idle": "2023-11-08T00:32:00.987678Z",
     "shell.execute_reply": "2023-11-08T00:32:00.987091Z"
    },
    "id": "TRFYHB2mCaWb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YSlYxFuRCaWk"
   },
   "source": [
    "每个标签都是一个 0 到 9 之间的整数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:00.990819Z",
     "iopub.status.busy": "2023-11-08T00:32:00.990571Z",
     "iopub.status.idle": "2023-11-08T00:32:00.995193Z",
     "shell.execute_reply": "2023-11-08T00:32:00.994575Z"
    },
    "id": "XKnCTHz4CaWg"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TMPI88iZpO2T"
   },
   "source": [
    "测试集中有 10,000 个图像。同样，每个图像都由 28x28 个像素表示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:00.998525Z",
     "iopub.status.busy": "2023-11-08T00:32:00.998271Z",
     "iopub.status.idle": "2023-11-08T00:32:01.002505Z",
     "shell.execute_reply": "2023-11-08T00:32:01.001852Z"
    },
    "id": "2KFnYlcwCaWl"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rd0A0Iu0CaWq"
   },
   "source": [
    "测试集包含 10,000 个图像标签："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:01.005749Z",
     "iopub.status.busy": "2023-11-08T00:32:01.005514Z",
     "iopub.status.idle": "2023-11-08T00:32:01.009770Z",
     "shell.execute_reply": "2023-11-08T00:32:01.009189Z"
    },
    "id": "iJmPr5-ACaWn"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ES6uQoLKCaWr"
   },
   "source": [
    "## 预处理数据\n",
    "\n",
    "在训练网络之前，必须对数据进行预处理。如果您检查训练集中的第一个图像，您会看到像素值处于 0 到 255 之间："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:01.013242Z",
     "iopub.status.busy": "2023-11-08T00:32:01.012605Z",
     "iopub.status.idle": "2023-11-08T00:32:01.206566Z",
     "shell.execute_reply": "2023-11-08T00:32:01.205852Z"
    },
    "id": "m4VEw8Ud9Quh"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wz7l27Lz9S1P"
   },
   "source": [
    "将这些值缩小至 0 到 1 之间，然后将其馈送到神经网络模型。为此，请将这些值除以 255。请务必以相同的方式对*训练集*和*测试集*进行预处理："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:01.210838Z",
     "iopub.status.busy": "2023-11-08T00:32:01.210205Z",
     "iopub.status.idle": "2023-11-08T00:32:01.405851Z",
     "shell.execute_reply": "2023-11-08T00:32:01.404750Z"
    },
    "id": "bW5WzIPlCaWv"
   },
   "outputs": [],
   "source": [
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ee638AlnCaWz"
   },
   "source": [
    "为了验证数据的格式是否正确，以及您是否已准备好构建和训练网络，让我们显示*训练集*中的前 25 个图像，并在每个图像下方显示类名称。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:01.410740Z",
     "iopub.status.busy": "2023-11-08T00:32:01.409959Z",
     "iopub.status.idle": "2023-11-08T00:32:02.283088Z",
     "shell.execute_reply": "2023-11-08T00:32:02.282325Z"
    },
    "id": "oZTImqg_CaW1"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "59veuiEZCaW4"
   },
   "source": [
    "## 构建模型\n",
    "\n",
    "构建神经网络需要先配置模型的层，然后再编译模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gxg1XGm0eOBy"
   },
   "source": [
    "### 设置层\n",
    "\n",
    "神经网络的基本组成部分是<em>层</em>。层会从向其馈送的数据中提取表示形式。希望这些表示形式有助于解决手头上的问题。\n",
    "\n",
    "大多数深度学习都包括将简单的层链接在一起。大多数层（如 `tf.keras.layers.Dense`）都具有在训练期间才会学习的参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:02.287662Z",
     "iopub.status.busy": "2023-11-08T00:32:02.287010Z",
     "iopub.status.idle": "2023-11-08T00:32:04.609753Z",
     "shell.execute_reply": "2023-11-08T00:32:04.608927Z"
    },
    "id": "9ODch-OFCaW4"
   },
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(10)\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gut8A_7rCaW6"
   },
   "source": [
    "该网络的第一层 `tf.keras.layers.Flatten` 将图像格式从二维数组（28 x 28 像素）转换成一维数组（28 x 28 = 784 像素）。将该层视为图像中未堆叠的像素行并将其排列起来。该层没有要学习的参数，它只会重新格式化数据。\n",
    "\n",
    "展平像素后，网络会包括两个 `tf.keras.layers.Dense` 层的序列。它们是密集连接或全连接神经层。第一个 `Dense` 层有 128 个节点（或神经元）。第二个（也是最后一个）层会返回一个长度为 10 的 logits 数组。每个节点都包含一个得分，用来表示当前图像属于 10 个类中的哪一类。\n",
    "\n",
    "### 编译模型\n",
    "\n",
    "在准备对模型进行训练之前，还需要再对其进行一些设置。以下内容是在模型的<em>编译</em>步骤中添加的：\n",
    "\n",
    "- <em>损失函数</em> - 测量模型在训练期间的准确程度。你希望最小化此函数，以便将模型“引导”到正确的方向上。\n",
    "- <em>优化器</em> - 决定模型如何根据其看到的数据和自身的损失函数进行更新。\n",
    "- <em>指标</em> - 用于监控训练和测试步骤。以下示例使用了*准确率*，即被正确分类的图像的比率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:04.614187Z",
     "iopub.status.busy": "2023-11-08T00:32:04.613898Z",
     "iopub.status.idle": "2023-11-08T00:32:04.630551Z",
     "shell.execute_reply": "2023-11-08T00:32:04.629851Z"
    },
    "id": "Lhan11blCaW7"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qKF6uW-BCaW-"
   },
   "source": [
    "## 训练模型\n",
    "\n",
    "训练神经网络模型需要执行以下步骤：\n",
    "\n",
    "1. 将训练数据馈送给模型。在本例中，训练数据位于 `train_images` 和 `train_labels` 数组中。\n",
    "2. 模型学习将图像和标签关联起来。\n",
    "3. 要求模型对测试集（在本例中为 `test_images` 数组）进行预测。\n",
    "4. 验证预测是否与 `test_labels` 数组中的标签相匹配。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z4P4zIV7E28Z"
   },
   "source": [
    "### 向模型馈送数据\n",
    "\n",
    "要开始训练，请调用 <code>model.fit</code> 方法，这样命名是因为该方法会将模型与训练数据进行“拟合”："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:04.634748Z",
     "iopub.status.busy": "2023-11-08T00:32:04.634068Z",
     "iopub.status.idle": "2023-11-08T00:32:48.326687Z",
     "shell.execute_reply": "2023-11-08T00:32:48.325933Z"
    },
    "id": "xvwvpA64CaW_"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1699403526.188243  897592 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 37:33 - loss: 2.2877 - accuracy: 0.1875"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  22/1875 [..............................] - ETA: 4s - loss: 1.4739 - accuracy: 0.5099   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  45/1875 [..............................] - ETA: 4s - loss: 1.1708 - accuracy: 0.6042"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  67/1875 [>.............................] - ETA: 4s - loss: 1.0255 - accuracy: 0.6516"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  89/1875 [>.............................] - ETA: 4s - loss: 0.9342 - accuracy: 0.6815"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 111/1875 [>.............................] - ETA: 4s - loss: 0.8816 - accuracy: 0.6976"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 133/1875 [=>............................] - ETA: 4s - loss: 0.8403 - accuracy: 0.7126"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 155/1875 [=>............................] - ETA: 3s - loss: 0.8162 - accuracy: 0.7188"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 178/1875 [=>............................] - ETA: 3s - loss: 0.7830 - accuracy: 0.7314"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 201/1875 [==>...........................] - ETA: 3s - loss: 0.7690 - accuracy: 0.7360"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 224/1875 [==>...........................] - ETA: 3s - loss: 0.7493 - accuracy: 0.7423"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 247/1875 [==>...........................] - ETA: 3s - loss: 0.7371 - accuracy: 0.7458"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 270/1875 [===>..........................] - ETA: 3s - loss: 0.7215 - accuracy: 0.7501"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 292/1875 [===>..........................] - ETA: 3s - loss: 0.7090 - accuracy: 0.7533"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 315/1875 [====>.........................] - ETA: 3s - loss: 0.6962 - accuracy: 0.7580"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 337/1875 [====>.........................] - ETA: 3s - loss: 0.6885 - accuracy: 0.7606"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 359/1875 [====>.........................] - ETA: 3s - loss: 0.6772 - accuracy: 0.7642"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 381/1875 [=====>........................] - ETA: 3s - loss: 0.6712 - accuracy: 0.7676"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 403/1875 [=====>........................] - ETA: 3s - loss: 0.6662 - accuracy: 0.7688"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 426/1875 [=====>........................] - ETA: 3s - loss: 0.6594 - accuracy: 0.7704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 449/1875 [======>.......................] - ETA: 3s - loss: 0.6513 - accuracy: 0.7739"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 472/1875 [======>.......................] - ETA: 3s - loss: 0.6455 - accuracy: 0.7766"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 494/1875 [======>.......................] - ETA: 3s - loss: 0.6384 - accuracy: 0.7788"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 517/1875 [=======>......................] - ETA: 3s - loss: 0.6341 - accuracy: 0.7806"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 540/1875 [=======>......................] - ETA: 3s - loss: 0.6259 - accuracy: 0.7836"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 562/1875 [=======>......................] - ETA: 2s - loss: 0.6216 - accuracy: 0.7850"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 584/1875 [========>.....................] - ETA: 2s - loss: 0.6179 - accuracy: 0.7859"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 606/1875 [========>.....................] - ETA: 2s - loss: 0.6137 - accuracy: 0.7874"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 628/1875 [=========>....................] - ETA: 2s - loss: 0.6092 - accuracy: 0.7892"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 651/1875 [=========>....................] - ETA: 2s - loss: 0.6050 - accuracy: 0.7901"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 674/1875 [=========>....................] - ETA: 2s - loss: 0.6010 - accuracy: 0.7915"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 697/1875 [==========>...................] - ETA: 2s - loss: 0.5987 - accuracy: 0.7922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 720/1875 [==========>...................] - ETA: 2s - loss: 0.5944 - accuracy: 0.7940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 742/1875 [==========>...................] - ETA: 2s - loss: 0.5884 - accuracy: 0.7963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 765/1875 [===========>..................] - ETA: 2s - loss: 0.5847 - accuracy: 0.7973"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 787/1875 [===========>..................] - ETA: 2s - loss: 0.5822 - accuracy: 0.7982"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 810/1875 [===========>..................] - ETA: 2s - loss: 0.5795 - accuracy: 0.7989"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 833/1875 [============>.................] - ETA: 2s - loss: 0.5781 - accuracy: 0.7993"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 856/1875 [============>.................] - ETA: 2s - loss: 0.5741 - accuracy: 0.8008"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 879/1875 [=============>................] - ETA: 2s - loss: 0.5733 - accuracy: 0.8009"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 903/1875 [=============>................] - ETA: 2s - loss: 0.5711 - accuracy: 0.8011"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 927/1875 [=============>................] - ETA: 2s - loss: 0.5696 - accuracy: 0.8015"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 950/1875 [==============>...............] - ETA: 2s - loss: 0.5663 - accuracy: 0.8028"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 973/1875 [==============>...............] - ETA: 2s - loss: 0.5639 - accuracy: 0.8033"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 996/1875 [==============>...............] - ETA: 1s - loss: 0.5621 - accuracy: 0.8038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1018/1875 [===============>..............] - ETA: 1s - loss: 0.5600 - accuracy: 0.8046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1041/1875 [===============>..............] - ETA: 1s - loss: 0.5569 - accuracy: 0.8054"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1064/1875 [================>.............] - ETA: 1s - loss: 0.5554 - accuracy: 0.8061"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1088/1875 [================>.............] - ETA: 1s - loss: 0.5535 - accuracy: 0.8067"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1112/1875 [================>.............] - ETA: 1s - loss: 0.5512 - accuracy: 0.8074"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1136/1875 [=================>............] - ETA: 1s - loss: 0.5491 - accuracy: 0.8081"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1160/1875 [=================>............] - ETA: 1s - loss: 0.5480 - accuracy: 0.8083"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1184/1875 [=================>............] - ETA: 1s - loss: 0.5459 - accuracy: 0.8089"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1207/1875 [==================>...........] - ETA: 1s - loss: 0.5439 - accuracy: 0.8096"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1230/1875 [==================>...........] - ETA: 1s - loss: 0.5425 - accuracy: 0.8101"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1254/1875 [===================>..........] - ETA: 1s - loss: 0.5417 - accuracy: 0.8102"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1278/1875 [===================>..........] - ETA: 1s - loss: 0.5388 - accuracy: 0.8112"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1302/1875 [===================>..........] - ETA: 1s - loss: 0.5367 - accuracy: 0.8120"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1326/1875 [====================>.........] - ETA: 1s - loss: 0.5339 - accuracy: 0.8130"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1350/1875 [====================>.........] - ETA: 1s - loss: 0.5319 - accuracy: 0.8139"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1374/1875 [====================>.........] - ETA: 1s - loss: 0.5305 - accuracy: 0.8144"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1398/1875 [=====================>........] - ETA: 1s - loss: 0.5287 - accuracy: 0.8148"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1422/1875 [=====================>........] - ETA: 1s - loss: 0.5270 - accuracy: 0.8153"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1446/1875 [======================>.......] - ETA: 0s - loss: 0.5247 - accuracy: 0.8160"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1470/1875 [======================>.......] - ETA: 0s - loss: 0.5219 - accuracy: 0.8169"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1494/1875 [======================>.......] - ETA: 0s - loss: 0.5201 - accuracy: 0.8177"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1518/1875 [=======================>......] - ETA: 0s - loss: 0.5185 - accuracy: 0.8181"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1542/1875 [=======================>......] - ETA: 0s - loss: 0.5161 - accuracy: 0.8188"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1566/1875 [========================>.....] - ETA: 0s - loss: 0.5149 - accuracy: 0.8192"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1590/1875 [========================>.....] - ETA: 0s - loss: 0.5130 - accuracy: 0.8199"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1614/1875 [========================>.....] - ETA: 0s - loss: 0.5116 - accuracy: 0.8204"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1638/1875 [=========================>....] - ETA: 0s - loss: 0.5107 - accuracy: 0.8207"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1662/1875 [=========================>....] - ETA: 0s - loss: 0.5094 - accuracy: 0.8213"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1685/1875 [=========================>....] - ETA: 0s - loss: 0.5077 - accuracy: 0.8219"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1708/1875 [==========================>...] - ETA: 0s - loss: 0.5060 - accuracy: 0.8224"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1731/1875 [==========================>...] - ETA: 0s - loss: 0.5044 - accuracy: 0.8229"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1754/1875 [===========================>..] - ETA: 0s - loss: 0.5029 - accuracy: 0.8232"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1777/1875 [===========================>..] - ETA: 0s - loss: 0.5026 - accuracy: 0.8232"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1800/1875 [===========================>..] - ETA: 0s - loss: 0.5011 - accuracy: 0.8237"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1824/1875 [============================>.] - ETA: 0s - loss: 0.4988 - accuracy: 0.8245"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1847/1875 [============================>.] - ETA: 0s - loss: 0.4982 - accuracy: 0.8248"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1870/1875 [============================>.] - ETA: 0s - loss: 0.4974 - accuracy: 0.8250"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4970 - accuracy: 0.8251\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 5s - loss: 0.3792 - accuracy: 0.8750"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  25/1875 [..............................] - ETA: 3s - loss: 0.3816 - accuracy: 0.8550"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  48/1875 [..............................] - ETA: 3s - loss: 0.3995 - accuracy: 0.8535"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  71/1875 [>.............................] - ETA: 3s - loss: 0.3925 - accuracy: 0.8600"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  94/1875 [>.............................] - ETA: 3s - loss: 0.4140 - accuracy: 0.8497"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 117/1875 [>.............................] - ETA: 3s - loss: 0.4066 - accuracy: 0.8502"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 140/1875 [=>............................] - ETA: 3s - loss: 0.4156 - accuracy: 0.8471"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 162/1875 [=>............................] - ETA: 3s - loss: 0.4211 - accuracy: 0.8447"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 185/1875 [=>............................] - ETA: 3s - loss: 0.4241 - accuracy: 0.8443"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 207/1875 [==>...........................] - ETA: 3s - loss: 0.4193 - accuracy: 0.8474"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 229/1875 [==>...........................] - ETA: 3s - loss: 0.4185 - accuracy: 0.8474"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 252/1875 [===>..........................] - ETA: 3s - loss: 0.4161 - accuracy: 0.8493"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 275/1875 [===>..........................] - ETA: 3s - loss: 0.4110 - accuracy: 0.8501"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 299/1875 [===>..........................] - ETA: 3s - loss: 0.4073 - accuracy: 0.8528"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 323/1875 [====>.........................] - ETA: 3s - loss: 0.4074 - accuracy: 0.8522"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 347/1875 [====>.........................] - ETA: 3s - loss: 0.4046 - accuracy: 0.8528"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 371/1875 [====>.........................] - ETA: 3s - loss: 0.4002 - accuracy: 0.8546"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 395/1875 [=====>........................] - ETA: 3s - loss: 0.3987 - accuracy: 0.8553"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 419/1875 [=====>........................] - ETA: 3s - loss: 0.3985 - accuracy: 0.8554"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 443/1875 [======>.......................] - ETA: 3s - loss: 0.3966 - accuracy: 0.8567"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 466/1875 [======>.......................] - ETA: 3s - loss: 0.3961 - accuracy: 0.8570"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 490/1875 [======>.......................] - ETA: 3s - loss: 0.3937 - accuracy: 0.8575"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 514/1875 [=======>......................] - ETA: 2s - loss: 0.3935 - accuracy: 0.8575"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 538/1875 [=======>......................] - ETA: 2s - loss: 0.3934 - accuracy: 0.8575"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 562/1875 [=======>......................] - ETA: 2s - loss: 0.3934 - accuracy: 0.8577"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 586/1875 [========>.....................] - ETA: 2s - loss: 0.3915 - accuracy: 0.8586"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 610/1875 [========>.....................] - ETA: 2s - loss: 0.3914 - accuracy: 0.8584"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 633/1875 [=========>....................] - ETA: 2s - loss: 0.3919 - accuracy: 0.8579"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 657/1875 [=========>....................] - ETA: 2s - loss: 0.3899 - accuracy: 0.8585"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 680/1875 [=========>....................] - ETA: 2s - loss: 0.3872 - accuracy: 0.8597"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 703/1875 [==========>...................] - ETA: 2s - loss: 0.3859 - accuracy: 0.8605"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 726/1875 [==========>...................] - ETA: 2s - loss: 0.3861 - accuracy: 0.8602"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 750/1875 [===========>..................] - ETA: 2s - loss: 0.3870 - accuracy: 0.8600"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 774/1875 [===========>..................] - ETA: 2s - loss: 0.3865 - accuracy: 0.8603"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 797/1875 [===========>..................] - ETA: 2s - loss: 0.3861 - accuracy: 0.8608"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 820/1875 [============>.................] - ETA: 2s - loss: 0.3872 - accuracy: 0.8603"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 843/1875 [============>.................] - ETA: 2s - loss: 0.3867 - accuracy: 0.8602"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 865/1875 [============>.................] - ETA: 2s - loss: 0.3861 - accuracy: 0.8604"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 887/1875 [=============>................] - ETA: 2s - loss: 0.3851 - accuracy: 0.8607"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 908/1875 [=============>................] - ETA: 2s - loss: 0.3845 - accuracy: 0.8607"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 930/1875 [=============>................] - ETA: 2s - loss: 0.3846 - accuracy: 0.8605"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 953/1875 [==============>...............] - ETA: 2s - loss: 0.3856 - accuracy: 0.8604"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 976/1875 [==============>...............] - ETA: 1s - loss: 0.3862 - accuracy: 0.8604"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1000/1875 [===============>..............] - ETA: 1s - loss: 0.3873 - accuracy: 0.8603"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1024/1875 [===============>..............] - ETA: 1s - loss: 0.3868 - accuracy: 0.8607"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1047/1875 [===============>..............] - ETA: 1s - loss: 0.3861 - accuracy: 0.8607"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1070/1875 [================>.............] - ETA: 1s - loss: 0.3858 - accuracy: 0.8609"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1094/1875 [================>.............] - ETA: 1s - loss: 0.3856 - accuracy: 0.8608"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1117/1875 [================>.............] - ETA: 1s - loss: 0.3849 - accuracy: 0.8611"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1140/1875 [=================>............] - ETA: 1s - loss: 0.3853 - accuracy: 0.8608"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1163/1875 [=================>............] - ETA: 1s - loss: 0.3845 - accuracy: 0.8611"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1187/1875 [=================>............] - ETA: 1s - loss: 0.3834 - accuracy: 0.8613"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1211/1875 [==================>...........] - ETA: 1s - loss: 0.3833 - accuracy: 0.8614"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1234/1875 [==================>...........] - ETA: 1s - loss: 0.3837 - accuracy: 0.8613"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1258/1875 [===================>..........] - ETA: 1s - loss: 0.3834 - accuracy: 0.8612"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1282/1875 [===================>..........] - ETA: 1s - loss: 0.3832 - accuracy: 0.8613"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1306/1875 [===================>..........] - ETA: 1s - loss: 0.3827 - accuracy: 0.8613"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1330/1875 [====================>.........] - ETA: 1s - loss: 0.3822 - accuracy: 0.8616"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1353/1875 [====================>.........] - ETA: 1s - loss: 0.3817 - accuracy: 0.8615"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1377/1875 [=====================>........] - ETA: 1s - loss: 0.3822 - accuracy: 0.8613"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1401/1875 [=====================>........] - ETA: 1s - loss: 0.3817 - accuracy: 0.8615"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1424/1875 [=====================>........] - ETA: 0s - loss: 0.3819 - accuracy: 0.8615"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1447/1875 [======================>.......] - ETA: 0s - loss: 0.3819 - accuracy: 0.8615"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1471/1875 [======================>.......] - ETA: 0s - loss: 0.3811 - accuracy: 0.8620"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1495/1875 [======================>.......] - ETA: 0s - loss: 0.3801 - accuracy: 0.8623"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1519/1875 [=======================>......] - ETA: 0s - loss: 0.3795 - accuracy: 0.8626"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1543/1875 [=======================>......] - ETA: 0s - loss: 0.3798 - accuracy: 0.8625"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1567/1875 [========================>.....] - ETA: 0s - loss: 0.3799 - accuracy: 0.8625"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1590/1875 [========================>.....] - ETA: 0s - loss: 0.3803 - accuracy: 0.8624"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1614/1875 [========================>.....] - ETA: 0s - loss: 0.3801 - accuracy: 0.8624"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1637/1875 [=========================>....] - ETA: 0s - loss: 0.3801 - accuracy: 0.8624"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1661/1875 [=========================>....] - ETA: 0s - loss: 0.3803 - accuracy: 0.8624"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1685/1875 [=========================>....] - ETA: 0s - loss: 0.3796 - accuracy: 0.8627"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1708/1875 [==========================>...] - ETA: 0s - loss: 0.3798 - accuracy: 0.8627"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1732/1875 [==========================>...] - ETA: 0s - loss: 0.3802 - accuracy: 0.8624"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1756/1875 [===========================>..] - ETA: 0s - loss: 0.3796 - accuracy: 0.8626"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1780/1875 [===========================>..] - ETA: 0s - loss: 0.3796 - accuracy: 0.8626"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1803/1875 [===========================>..] - ETA: 0s - loss: 0.3790 - accuracy: 0.8627"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1826/1875 [============================>.] - ETA: 0s - loss: 0.3782 - accuracy: 0.8630"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1850/1875 [============================>.] - ETA: 0s - loss: 0.3780 - accuracy: 0.8630"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1874/1875 [============================>.] - ETA: 0s - loss: 0.3780 - accuracy: 0.8630"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3780 - accuracy: 0.8631\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 5s - loss: 0.1599 - accuracy: 0.9062"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  25/1875 [..............................] - ETA: 3s - loss: 0.3453 - accuracy: 0.8662"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  49/1875 [..............................] - ETA: 3s - loss: 0.3509 - accuracy: 0.8680"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  73/1875 [>.............................] - ETA: 3s - loss: 0.3429 - accuracy: 0.8729"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  97/1875 [>.............................] - ETA: 3s - loss: 0.3535 - accuracy: 0.8692"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 121/1875 [>.............................] - ETA: 3s - loss: 0.3477 - accuracy: 0.8727"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 145/1875 [=>............................] - ETA: 3s - loss: 0.3479 - accuracy: 0.8739"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 169/1875 [=>............................] - ETA: 3s - loss: 0.3415 - accuracy: 0.8752"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 193/1875 [==>...........................] - ETA: 3s - loss: 0.3417 - accuracy: 0.8761"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 216/1875 [==>...........................] - ETA: 3s - loss: 0.3364 - accuracy: 0.8776"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 240/1875 [==>...........................] - ETA: 3s - loss: 0.3327 - accuracy: 0.8783"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 263/1875 [===>..........................] - ETA: 3s - loss: 0.3333 - accuracy: 0.8776"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 286/1875 [===>..........................] - ETA: 3s - loss: 0.3350 - accuracy: 0.8771"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 310/1875 [===>..........................] - ETA: 3s - loss: 0.3330 - accuracy: 0.8780"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 333/1875 [====>.........................] - ETA: 3s - loss: 0.3306 - accuracy: 0.8788"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 356/1875 [====>.........................] - ETA: 3s - loss: 0.3297 - accuracy: 0.8790"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 380/1875 [=====>........................] - ETA: 3s - loss: 0.3312 - accuracy: 0.8793"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 403/1875 [=====>........................] - ETA: 3s - loss: 0.3301 - accuracy: 0.8800"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 426/1875 [=====>........................] - ETA: 3s - loss: 0.3321 - accuracy: 0.8796"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 449/1875 [======>.......................] - ETA: 3s - loss: 0.3317 - accuracy: 0.8795"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 472/1875 [======>.......................] - ETA: 3s - loss: 0.3328 - accuracy: 0.8791"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 495/1875 [======>.......................] - ETA: 3s - loss: 0.3337 - accuracy: 0.8787"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 518/1875 [=======>......................] - ETA: 2s - loss: 0.3348 - accuracy: 0.8784"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 541/1875 [=======>......................] - ETA: 2s - loss: 0.3356 - accuracy: 0.8779"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 564/1875 [========>.....................] - ETA: 2s - loss: 0.3356 - accuracy: 0.8780"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 587/1875 [========>.....................] - ETA: 2s - loss: 0.3355 - accuracy: 0.8781"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 610/1875 [========>.....................] - ETA: 2s - loss: 0.3342 - accuracy: 0.8786"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 633/1875 [=========>....................] - ETA: 2s - loss: 0.3357 - accuracy: 0.8780"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 655/1875 [=========>....................] - ETA: 2s - loss: 0.3371 - accuracy: 0.8779"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 677/1875 [=========>....................] - ETA: 2s - loss: 0.3359 - accuracy: 0.8781"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 700/1875 [==========>...................] - ETA: 2s - loss: 0.3376 - accuracy: 0.8782"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 722/1875 [==========>...................] - ETA: 2s - loss: 0.3377 - accuracy: 0.8784"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 745/1875 [==========>...................] - ETA: 2s - loss: 0.3386 - accuracy: 0.8782"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 767/1875 [===========>..................] - ETA: 2s - loss: 0.3386 - accuracy: 0.8783"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 789/1875 [===========>..................] - ETA: 2s - loss: 0.3377 - accuracy: 0.8788"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 811/1875 [===========>..................] - ETA: 2s - loss: 0.3389 - accuracy: 0.8789"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 833/1875 [============>.................] - ETA: 2s - loss: 0.3397 - accuracy: 0.8788"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 856/1875 [============>.................] - ETA: 2s - loss: 0.3391 - accuracy: 0.8789"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 878/1875 [=============>................] - ETA: 2s - loss: 0.3387 - accuracy: 0.8791"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 901/1875 [=============>................] - ETA: 2s - loss: 0.3385 - accuracy: 0.8790"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 923/1875 [=============>................] - ETA: 2s - loss: 0.3387 - accuracy: 0.8790"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 946/1875 [==============>...............] - ETA: 2s - loss: 0.3388 - accuracy: 0.8788"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 969/1875 [==============>...............] - ETA: 2s - loss: 0.3395 - accuracy: 0.8785"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 992/1875 [==============>...............] - ETA: 1s - loss: 0.3389 - accuracy: 0.8787"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1015/1875 [===============>..............] - ETA: 1s - loss: 0.3382 - accuracy: 0.8789"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1037/1875 [===============>..............] - ETA: 1s - loss: 0.3388 - accuracy: 0.8786"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1059/1875 [===============>..............] - ETA: 1s - loss: 0.3388 - accuracy: 0.8788"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1082/1875 [================>.............] - ETA: 1s - loss: 0.3379 - accuracy: 0.8790"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1105/1875 [================>.............] - ETA: 1s - loss: 0.3376 - accuracy: 0.8792"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1128/1875 [=================>............] - ETA: 1s - loss: 0.3370 - accuracy: 0.8793"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1151/1875 [=================>............] - ETA: 1s - loss: 0.3361 - accuracy: 0.8795"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1174/1875 [=================>............] - ETA: 1s - loss: 0.3355 - accuracy: 0.8798"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1196/1875 [==================>...........] - ETA: 1s - loss: 0.3353 - accuracy: 0.8797"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1219/1875 [==================>...........] - ETA: 1s - loss: 0.3359 - accuracy: 0.8791"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1242/1875 [==================>...........] - ETA: 1s - loss: 0.3356 - accuracy: 0.8792"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1265/1875 [===================>..........] - ETA: 1s - loss: 0.3370 - accuracy: 0.8787"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1287/1875 [===================>..........] - ETA: 1s - loss: 0.3377 - accuracy: 0.8783"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1310/1875 [===================>..........] - ETA: 1s - loss: 0.3374 - accuracy: 0.8783"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1333/1875 [====================>.........] - ETA: 1s - loss: 0.3369 - accuracy: 0.8784"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1355/1875 [====================>.........] - ETA: 1s - loss: 0.3370 - accuracy: 0.8784"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1377/1875 [=====================>........] - ETA: 1s - loss: 0.3374 - accuracy: 0.8783"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1399/1875 [=====================>........] - ETA: 1s - loss: 0.3374 - accuracy: 0.8785"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1422/1875 [=====================>........] - ETA: 1s - loss: 0.3373 - accuracy: 0.8784"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1445/1875 [======================>.......] - ETA: 0s - loss: 0.3372 - accuracy: 0.8783"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1468/1875 [======================>.......] - ETA: 0s - loss: 0.3374 - accuracy: 0.8784"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1490/1875 [======================>.......] - ETA: 0s - loss: 0.3375 - accuracy: 0.8781"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1513/1875 [=======================>......] - ETA: 0s - loss: 0.3377 - accuracy: 0.8780"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1536/1875 [=======================>......] - ETA: 0s - loss: 0.3375 - accuracy: 0.8781"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1558/1875 [=======================>......] - ETA: 0s - loss: 0.3375 - accuracy: 0.8781"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1580/1875 [========================>.....] - ETA: 0s - loss: 0.3376 - accuracy: 0.8779"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1603/1875 [========================>.....] - ETA: 0s - loss: 0.3375 - accuracy: 0.8779"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1625/1875 [=========================>....] - ETA: 0s - loss: 0.3374 - accuracy: 0.8780"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1647/1875 [=========================>....] - ETA: 0s - loss: 0.3374 - accuracy: 0.8777"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1670/1875 [=========================>....] - ETA: 0s - loss: 0.3376 - accuracy: 0.8776"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1694/1875 [==========================>...] - ETA: 0s - loss: 0.3379 - accuracy: 0.8777"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1717/1875 [==========================>...] - ETA: 0s - loss: 0.3379 - accuracy: 0.8777"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1739/1875 [==========================>...] - ETA: 0s - loss: 0.3373 - accuracy: 0.8778"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1762/1875 [===========================>..] - ETA: 0s - loss: 0.3376 - accuracy: 0.8777"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1785/1875 [===========================>..] - ETA: 0s - loss: 0.3377 - accuracy: 0.8774"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1808/1875 [===========================>..] - ETA: 0s - loss: 0.3378 - accuracy: 0.8773"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1831/1875 [============================>.] - ETA: 0s - loss: 0.3377 - accuracy: 0.8773"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1854/1875 [============================>.] - ETA: 0s - loss: 0.3373 - accuracy: 0.8776"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3374 - accuracy: 0.8776\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 5s - loss: 0.2594 - accuracy: 0.8750"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  24/1875 [..............................] - ETA: 4s - loss: 0.3302 - accuracy: 0.8906"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  47/1875 [..............................] - ETA: 4s - loss: 0.3097 - accuracy: 0.8963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  70/1875 [>.............................] - ETA: 4s - loss: 0.3129 - accuracy: 0.8911"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  92/1875 [>.............................] - ETA: 3s - loss: 0.3155 - accuracy: 0.8886"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 115/1875 [>.............................] - ETA: 3s - loss: 0.3046 - accuracy: 0.8916"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 137/1875 [=>............................] - ETA: 3s - loss: 0.3071 - accuracy: 0.8885"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 160/1875 [=>............................] - ETA: 3s - loss: 0.3116 - accuracy: 0.8857"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 182/1875 [=>............................] - ETA: 3s - loss: 0.3151 - accuracy: 0.8838"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 205/1875 [==>...........................] - ETA: 3s - loss: 0.3172 - accuracy: 0.8829"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 227/1875 [==>...........................] - ETA: 3s - loss: 0.3169 - accuracy: 0.8831"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 249/1875 [==>...........................] - ETA: 3s - loss: 0.3186 - accuracy: 0.8817"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 272/1875 [===>..........................] - ETA: 3s - loss: 0.3186 - accuracy: 0.8814"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 294/1875 [===>..........................] - ETA: 3s - loss: 0.3217 - accuracy: 0.8806"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 317/1875 [====>.........................] - ETA: 3s - loss: 0.3221 - accuracy: 0.8812"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 340/1875 [====>.........................] - ETA: 3s - loss: 0.3237 - accuracy: 0.8814"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 363/1875 [====>.........................] - ETA: 3s - loss: 0.3256 - accuracy: 0.8814"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 386/1875 [=====>........................] - ETA: 3s - loss: 0.3242 - accuracy: 0.8821"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 409/1875 [=====>........................] - ETA: 3s - loss: 0.3229 - accuracy: 0.8832"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 432/1875 [=====>........................] - ETA: 3s - loss: 0.3228 - accuracy: 0.8832"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 455/1875 [======>.......................] - ETA: 3s - loss: 0.3232 - accuracy: 0.8830"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 478/1875 [======>.......................] - ETA: 3s - loss: 0.3225 - accuracy: 0.8830"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 502/1875 [=======>......................] - ETA: 3s - loss: 0.3213 - accuracy: 0.8830"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 526/1875 [=======>......................] - ETA: 3s - loss: 0.3212 - accuracy: 0.8832"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 549/1875 [=======>......................] - ETA: 2s - loss: 0.3190 - accuracy: 0.8837"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 572/1875 [========>.....................] - ETA: 2s - loss: 0.3198 - accuracy: 0.8832"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 595/1875 [========>.....................] - ETA: 2s - loss: 0.3191 - accuracy: 0.8836"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 617/1875 [========>.....................] - ETA: 2s - loss: 0.3198 - accuracy: 0.8836"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 640/1875 [=========>....................] - ETA: 2s - loss: 0.3207 - accuracy: 0.8835"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 662/1875 [=========>....................] - ETA: 2s - loss: 0.3204 - accuracy: 0.8832"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 685/1875 [=========>....................] - ETA: 2s - loss: 0.3190 - accuracy: 0.8836"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 708/1875 [==========>...................] - ETA: 2s - loss: 0.3192 - accuracy: 0.8836"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 731/1875 [==========>...................] - ETA: 2s - loss: 0.3181 - accuracy: 0.8842"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 753/1875 [===========>..................] - ETA: 2s - loss: 0.3196 - accuracy: 0.8838"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 776/1875 [===========>..................] - ETA: 2s - loss: 0.3194 - accuracy: 0.8837"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 799/1875 [===========>..................] - ETA: 2s - loss: 0.3187 - accuracy: 0.8840"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 822/1875 [============>.................] - ETA: 2s - loss: 0.3175 - accuracy: 0.8841"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 844/1875 [============>.................] - ETA: 2s - loss: 0.3175 - accuracy: 0.8841"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 866/1875 [============>.................] - ETA: 2s - loss: 0.3172 - accuracy: 0.8842"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 888/1875 [=============>................] - ETA: 2s - loss: 0.3163 - accuracy: 0.8844"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 910/1875 [=============>................] - ETA: 2s - loss: 0.3168 - accuracy: 0.8842"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 932/1875 [=============>................] - ETA: 2s - loss: 0.3162 - accuracy: 0.8842"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 955/1875 [==============>...............] - ETA: 2s - loss: 0.3155 - accuracy: 0.8843"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 977/1875 [==============>...............] - ETA: 2s - loss: 0.3158 - accuracy: 0.8841"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1000/1875 [===============>..............] - ETA: 1s - loss: 0.3158 - accuracy: 0.8845"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1022/1875 [===============>..............] - ETA: 1s - loss: 0.3155 - accuracy: 0.8847"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1045/1875 [===============>..............] - ETA: 1s - loss: 0.3154 - accuracy: 0.8847"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1068/1875 [================>.............] - ETA: 1s - loss: 0.3149 - accuracy: 0.8849"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1090/1875 [================>.............] - ETA: 1s - loss: 0.3152 - accuracy: 0.8849"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1112/1875 [================>.............] - ETA: 1s - loss: 0.3150 - accuracy: 0.8851"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1135/1875 [=================>............] - ETA: 1s - loss: 0.3149 - accuracy: 0.8851"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1158/1875 [=================>............] - ETA: 1s - loss: 0.3147 - accuracy: 0.8852"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1180/1875 [=================>............] - ETA: 1s - loss: 0.3164 - accuracy: 0.8846"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1203/1875 [==================>...........] - ETA: 1s - loss: 0.3166 - accuracy: 0.8847"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1227/1875 [==================>...........] - ETA: 1s - loss: 0.3163 - accuracy: 0.8849"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1250/1875 [===================>..........] - ETA: 1s - loss: 0.3160 - accuracy: 0.8851"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1273/1875 [===================>..........] - ETA: 1s - loss: 0.3162 - accuracy: 0.8850"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1296/1875 [===================>..........] - ETA: 1s - loss: 0.3164 - accuracy: 0.8851"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1319/1875 [====================>.........] - ETA: 1s - loss: 0.3155 - accuracy: 0.8853"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1342/1875 [====================>.........] - ETA: 1s - loss: 0.3146 - accuracy: 0.8858"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1365/1875 [====================>.........] - ETA: 1s - loss: 0.3142 - accuracy: 0.8858"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1388/1875 [=====================>........] - ETA: 1s - loss: 0.3140 - accuracy: 0.8858"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1411/1875 [=====================>........] - ETA: 1s - loss: 0.3145 - accuracy: 0.8859"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1434/1875 [=====================>........] - ETA: 0s - loss: 0.3150 - accuracy: 0.8857"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1456/1875 [======================>.......] - ETA: 0s - loss: 0.3146 - accuracy: 0.8857"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1479/1875 [======================>.......] - ETA: 0s - loss: 0.3152 - accuracy: 0.8855"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1502/1875 [=======================>......] - ETA: 0s - loss: 0.3148 - accuracy: 0.8857"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1525/1875 [=======================>......] - ETA: 0s - loss: 0.3155 - accuracy: 0.8854"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1549/1875 [=======================>......] - ETA: 0s - loss: 0.3152 - accuracy: 0.8855"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1572/1875 [========================>.....] - ETA: 0s - loss: 0.3156 - accuracy: 0.8854"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1595/1875 [========================>.....] - ETA: 0s - loss: 0.3158 - accuracy: 0.8853"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1618/1875 [========================>.....] - ETA: 0s - loss: 0.3160 - accuracy: 0.8852"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1641/1875 [=========================>....] - ETA: 0s - loss: 0.3154 - accuracy: 0.8855"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1664/1875 [=========================>....] - ETA: 0s - loss: 0.3154 - accuracy: 0.8854"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1687/1875 [=========================>....] - ETA: 0s - loss: 0.3155 - accuracy: 0.8854"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1710/1875 [==========================>...] - ETA: 0s - loss: 0.3148 - accuracy: 0.8857"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1733/1875 [==========================>...] - ETA: 0s - loss: 0.3147 - accuracy: 0.8856"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1756/1875 [===========================>..] - ETA: 0s - loss: 0.3144 - accuracy: 0.8857"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1779/1875 [===========================>..] - ETA: 0s - loss: 0.3143 - accuracy: 0.8857"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1802/1875 [===========================>..] - ETA: 0s - loss: 0.3146 - accuracy: 0.8857"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1825/1875 [============================>.] - ETA: 0s - loss: 0.3150 - accuracy: 0.8855"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1848/1875 [============================>.] - ETA: 0s - loss: 0.3151 - accuracy: 0.8854"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1871/1875 [============================>.] - ETA: 0s - loss: 0.3147 - accuracy: 0.8857"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3147 - accuracy: 0.8857\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 5s - loss: 0.1982 - accuracy: 0.8750"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  24/1875 [..............................] - ETA: 4s - loss: 0.2783 - accuracy: 0.8945"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  47/1875 [..............................] - ETA: 4s - loss: 0.2889 - accuracy: 0.8883"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  70/1875 [>.............................] - ETA: 4s - loss: 0.2995 - accuracy: 0.8884"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  93/1875 [>.............................] - ETA: 4s - loss: 0.2984 - accuracy: 0.8884"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 115/1875 [>.............................] - ETA: 3s - loss: 0.2946 - accuracy: 0.8886"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 138/1875 [=>............................] - ETA: 3s - loss: 0.2957 - accuracy: 0.8904"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 161/1875 [=>............................] - ETA: 3s - loss: 0.3000 - accuracy: 0.8901"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 184/1875 [=>............................] - ETA: 3s - loss: 0.3056 - accuracy: 0.8877"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 207/1875 [==>...........................] - ETA: 3s - loss: 0.3084 - accuracy: 0.8860"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 230/1875 [==>...........................] - ETA: 3s - loss: 0.3128 - accuracy: 0.8834"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 253/1875 [===>..........................] - ETA: 3s - loss: 0.3113 - accuracy: 0.8843"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 276/1875 [===>..........................] - ETA: 3s - loss: 0.3118 - accuracy: 0.8839"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 299/1875 [===>..........................] - ETA: 3s - loss: 0.3105 - accuracy: 0.8841"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 322/1875 [====>.........................] - ETA: 3s - loss: 0.3098 - accuracy: 0.8844"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 345/1875 [====>.........................] - ETA: 3s - loss: 0.3090 - accuracy: 0.8843"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 368/1875 [====>.........................] - ETA: 3s - loss: 0.3090 - accuracy: 0.8849"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 391/1875 [=====>........................] - ETA: 3s - loss: 0.3080 - accuracy: 0.8857"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 414/1875 [=====>........................] - ETA: 3s - loss: 0.3078 - accuracy: 0.8867"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 437/1875 [=====>........................] - ETA: 3s - loss: 0.3065 - accuracy: 0.8870"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 460/1875 [======>.......................] - ETA: 3s - loss: 0.3065 - accuracy: 0.8874"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 483/1875 [======>.......................] - ETA: 3s - loss: 0.3060 - accuracy: 0.8878"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 506/1875 [=======>......................] - ETA: 3s - loss: 0.3055 - accuracy: 0.8883"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 529/1875 [=======>......................] - ETA: 3s - loss: 0.3058 - accuracy: 0.8886"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 552/1875 [=======>......................] - ETA: 2s - loss: 0.3058 - accuracy: 0.8885"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 575/1875 [========>.....................] - ETA: 2s - loss: 0.3051 - accuracy: 0.8886"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 599/1875 [========>.....................] - ETA: 2s - loss: 0.3055 - accuracy: 0.8884"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 622/1875 [========>.....................] - ETA: 2s - loss: 0.3045 - accuracy: 0.8886"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 645/1875 [=========>....................] - ETA: 2s - loss: 0.3050 - accuracy: 0.8883"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 668/1875 [=========>....................] - ETA: 2s - loss: 0.3054 - accuracy: 0.8883"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 691/1875 [==========>...................] - ETA: 2s - loss: 0.3040 - accuracy: 0.8881"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 714/1875 [==========>...................] - ETA: 2s - loss: 0.3025 - accuracy: 0.8887"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 737/1875 [==========>...................] - ETA: 2s - loss: 0.3009 - accuracy: 0.8894"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 760/1875 [===========>..................] - ETA: 2s - loss: 0.3020 - accuracy: 0.8893"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 783/1875 [===========>..................] - ETA: 2s - loss: 0.3025 - accuracy: 0.8891"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 806/1875 [===========>..................] - ETA: 2s - loss: 0.3031 - accuracy: 0.8890"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 829/1875 [============>.................] - ETA: 2s - loss: 0.3012 - accuracy: 0.8899"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 852/1875 [============>.................] - ETA: 2s - loss: 0.3005 - accuracy: 0.8901"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 875/1875 [=============>................] - ETA: 2s - loss: 0.3006 - accuracy: 0.8896"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 898/1875 [=============>................] - ETA: 2s - loss: 0.3006 - accuracy: 0.8897"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 921/1875 [=============>................] - ETA: 2s - loss: 0.2994 - accuracy: 0.8902"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 944/1875 [==============>...............] - ETA: 2s - loss: 0.2996 - accuracy: 0.8904"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 967/1875 [==============>...............] - ETA: 2s - loss: 0.2992 - accuracy: 0.8906"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 990/1875 [==============>...............] - ETA: 1s - loss: 0.2998 - accuracy: 0.8903"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1013/1875 [===============>..............] - ETA: 1s - loss: 0.3004 - accuracy: 0.8903"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1037/1875 [===============>..............] - ETA: 1s - loss: 0.3004 - accuracy: 0.8902"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1061/1875 [===============>..............] - ETA: 1s - loss: 0.3002 - accuracy: 0.8902"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1085/1875 [================>.............] - ETA: 1s - loss: 0.2996 - accuracy: 0.8901"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1109/1875 [================>.............] - ETA: 1s - loss: 0.3001 - accuracy: 0.8899"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1132/1875 [=================>............] - ETA: 1s - loss: 0.2997 - accuracy: 0.8902"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1155/1875 [=================>............] - ETA: 1s - loss: 0.3001 - accuracy: 0.8898"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1179/1875 [=================>............] - ETA: 1s - loss: 0.3003 - accuracy: 0.8898"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1203/1875 [==================>...........] - ETA: 1s - loss: 0.2995 - accuracy: 0.8901"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1225/1875 [==================>...........] - ETA: 1s - loss: 0.2986 - accuracy: 0.8905"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1248/1875 [==================>...........] - ETA: 1s - loss: 0.2979 - accuracy: 0.8908"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1271/1875 [===================>..........] - ETA: 1s - loss: 0.2979 - accuracy: 0.8907"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1294/1875 [===================>..........] - ETA: 1s - loss: 0.2981 - accuracy: 0.8905"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1318/1875 [====================>.........] - ETA: 1s - loss: 0.2980 - accuracy: 0.8905"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1342/1875 [====================>.........] - ETA: 1s - loss: 0.2982 - accuracy: 0.8904"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1365/1875 [====================>.........] - ETA: 1s - loss: 0.2979 - accuracy: 0.8903"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1388/1875 [=====================>........] - ETA: 1s - loss: 0.2975 - accuracy: 0.8903"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1411/1875 [=====================>........] - ETA: 1s - loss: 0.2973 - accuracy: 0.8904"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1434/1875 [=====================>........] - ETA: 0s - loss: 0.2976 - accuracy: 0.8903"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1457/1875 [======================>.......] - ETA: 0s - loss: 0.2973 - accuracy: 0.8904"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1479/1875 [======================>.......] - ETA: 0s - loss: 0.2971 - accuracy: 0.8905"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1502/1875 [=======================>......] - ETA: 0s - loss: 0.2966 - accuracy: 0.8905"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1526/1875 [=======================>......] - ETA: 0s - loss: 0.2972 - accuracy: 0.8902"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1550/1875 [=======================>......] - ETA: 0s - loss: 0.2974 - accuracy: 0.8902"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1573/1875 [========================>.....] - ETA: 0s - loss: 0.2974 - accuracy: 0.8903"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1596/1875 [========================>.....] - ETA: 0s - loss: 0.2967 - accuracy: 0.8905"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1620/1875 [========================>.....] - ETA: 0s - loss: 0.2968 - accuracy: 0.8905"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1644/1875 [=========================>....] - ETA: 0s - loss: 0.2964 - accuracy: 0.8907"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1668/1875 [=========================>....] - ETA: 0s - loss: 0.2960 - accuracy: 0.8909"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1691/1875 [==========================>...] - ETA: 0s - loss: 0.2960 - accuracy: 0.8909"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1714/1875 [==========================>...] - ETA: 0s - loss: 0.2959 - accuracy: 0.8909"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1737/1875 [==========================>...] - ETA: 0s - loss: 0.2959 - accuracy: 0.8908"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1760/1875 [===========================>..] - ETA: 0s - loss: 0.2964 - accuracy: 0.8907"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1783/1875 [===========================>..] - ETA: 0s - loss: 0.2963 - accuracy: 0.8905"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1807/1875 [===========================>..] - ETA: 0s - loss: 0.2969 - accuracy: 0.8904"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1831/1875 [============================>.] - ETA: 0s - loss: 0.2966 - accuracy: 0.8905"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1855/1875 [============================>.] - ETA: 0s - loss: 0.2964 - accuracy: 0.8906"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2957 - accuracy: 0.8910\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 5s - loss: 0.4928 - accuracy: 0.8438"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  24/1875 [..............................] - ETA: 4s - loss: 0.2643 - accuracy: 0.8945"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  47/1875 [..............................] - ETA: 4s - loss: 0.2758 - accuracy: 0.8989"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  71/1875 [>.............................] - ETA: 3s - loss: 0.2686 - accuracy: 0.9001"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  94/1875 [>.............................] - ETA: 3s - loss: 0.2705 - accuracy: 0.8953"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 117/1875 [>.............................] - ETA: 3s - loss: 0.2764 - accuracy: 0.8948"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 140/1875 [=>............................] - ETA: 3s - loss: 0.2764 - accuracy: 0.8962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 164/1875 [=>............................] - ETA: 3s - loss: 0.2802 - accuracy: 0.8946"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 188/1875 [==>...........................] - ETA: 3s - loss: 0.2893 - accuracy: 0.8913"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 211/1875 [==>...........................] - ETA: 3s - loss: 0.2869 - accuracy: 0.8917"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 234/1875 [==>...........................] - ETA: 3s - loss: 0.2845 - accuracy: 0.8929"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 258/1875 [===>..........................] - ETA: 3s - loss: 0.2837 - accuracy: 0.8939"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 282/1875 [===>..........................] - ETA: 3s - loss: 0.2844 - accuracy: 0.8928"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 306/1875 [===>..........................] - ETA: 3s - loss: 0.2801 - accuracy: 0.8950"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 330/1875 [====>.........................] - ETA: 3s - loss: 0.2804 - accuracy: 0.8957"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 355/1875 [====>.........................] - ETA: 3s - loss: 0.2803 - accuracy: 0.8960"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 377/1875 [=====>........................] - ETA: 3s - loss: 0.2789 - accuracy: 0.8961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 400/1875 [=====>........................] - ETA: 3s - loss: 0.2808 - accuracy: 0.8959"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 423/1875 [=====>........................] - ETA: 3s - loss: 0.2780 - accuracy: 0.8966"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 447/1875 [======>.......................] - ETA: 3s - loss: 0.2769 - accuracy: 0.8963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 471/1875 [======>.......................] - ETA: 3s - loss: 0.2786 - accuracy: 0.8950"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 495/1875 [======>.......................] - ETA: 2s - loss: 0.2811 - accuracy: 0.8945"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 518/1875 [=======>......................] - ETA: 2s - loss: 0.2791 - accuracy: 0.8951"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 541/1875 [=======>......................] - ETA: 2s - loss: 0.2778 - accuracy: 0.8953"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 564/1875 [========>.....................] - ETA: 2s - loss: 0.2782 - accuracy: 0.8953"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 587/1875 [========>.....................] - ETA: 2s - loss: 0.2783 - accuracy: 0.8952"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 610/1875 [========>.....................] - ETA: 2s - loss: 0.2759 - accuracy: 0.8967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 633/1875 [=========>....................] - ETA: 2s - loss: 0.2757 - accuracy: 0.8970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 656/1875 [=========>....................] - ETA: 2s - loss: 0.2753 - accuracy: 0.8967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 679/1875 [=========>....................] - ETA: 2s - loss: 0.2756 - accuracy: 0.8970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 702/1875 [==========>...................] - ETA: 2s - loss: 0.2770 - accuracy: 0.8966"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 727/1875 [==========>...................] - ETA: 2s - loss: 0.2772 - accuracy: 0.8968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 751/1875 [===========>..................] - ETA: 2s - loss: 0.2777 - accuracy: 0.8966"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 774/1875 [===========>..................] - ETA: 2s - loss: 0.2788 - accuracy: 0.8964"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 797/1875 [===========>..................] - ETA: 2s - loss: 0.2783 - accuracy: 0.8966"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 820/1875 [============>.................] - ETA: 2s - loss: 0.2797 - accuracy: 0.8960"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 843/1875 [============>.................] - ETA: 2s - loss: 0.2804 - accuracy: 0.8955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 866/1875 [============>.................] - ETA: 2s - loss: 0.2803 - accuracy: 0.8957"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 889/1875 [=============>................] - ETA: 2s - loss: 0.2802 - accuracy: 0.8956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 913/1875 [=============>................] - ETA: 2s - loss: 0.2816 - accuracy: 0.8953"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 936/1875 [=============>................] - ETA: 2s - loss: 0.2811 - accuracy: 0.8954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 959/1875 [==============>...............] - ETA: 1s - loss: 0.2803 - accuracy: 0.8958"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 982/1875 [==============>...............] - ETA: 1s - loss: 0.2798 - accuracy: 0.8960"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1005/1875 [===============>..............] - ETA: 1s - loss: 0.2798 - accuracy: 0.8961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1028/1875 [===============>..............] - ETA: 1s - loss: 0.2800 - accuracy: 0.8960"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1051/1875 [===============>..............] - ETA: 1s - loss: 0.2791 - accuracy: 0.8964"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1074/1875 [================>.............] - ETA: 1s - loss: 0.2792 - accuracy: 0.8966"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1098/1875 [================>.............] - ETA: 1s - loss: 0.2784 - accuracy: 0.8965"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1122/1875 [================>.............] - ETA: 1s - loss: 0.2793 - accuracy: 0.8960"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1146/1875 [=================>............] - ETA: 1s - loss: 0.2796 - accuracy: 0.8961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1169/1875 [=================>............] - ETA: 1s - loss: 0.2787 - accuracy: 0.8964"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1192/1875 [==================>...........] - ETA: 1s - loss: 0.2792 - accuracy: 0.8962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1215/1875 [==================>...........] - ETA: 1s - loss: 0.2802 - accuracy: 0.8959"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1238/1875 [==================>...........] - ETA: 1s - loss: 0.2811 - accuracy: 0.8955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1261/1875 [===================>..........] - ETA: 1s - loss: 0.2816 - accuracy: 0.8954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1284/1875 [===================>..........] - ETA: 1s - loss: 0.2814 - accuracy: 0.8956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1307/1875 [===================>..........] - ETA: 1s - loss: 0.2823 - accuracy: 0.8953"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1331/1875 [====================>.........] - ETA: 1s - loss: 0.2823 - accuracy: 0.8953"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1354/1875 [====================>.........] - ETA: 1s - loss: 0.2823 - accuracy: 0.8953"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1378/1875 [=====================>........] - ETA: 1s - loss: 0.2820 - accuracy: 0.8954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1401/1875 [=====================>........] - ETA: 1s - loss: 0.2817 - accuracy: 0.8956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1424/1875 [=====================>........] - ETA: 0s - loss: 0.2813 - accuracy: 0.8959"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1447/1875 [======================>.......] - ETA: 0s - loss: 0.2811 - accuracy: 0.8960"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1469/1875 [======================>.......] - ETA: 0s - loss: 0.2812 - accuracy: 0.8960"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1492/1875 [======================>.......] - ETA: 0s - loss: 0.2813 - accuracy: 0.8959"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1515/1875 [=======================>......] - ETA: 0s - loss: 0.2818 - accuracy: 0.8955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1538/1875 [=======================>......] - ETA: 0s - loss: 0.2816 - accuracy: 0.8956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1561/1875 [=======================>......] - ETA: 0s - loss: 0.2819 - accuracy: 0.8954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1584/1875 [========================>.....] - ETA: 0s - loss: 0.2819 - accuracy: 0.8955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1607/1875 [========================>.....] - ETA: 0s - loss: 0.2824 - accuracy: 0.8956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1630/1875 [=========================>....] - ETA: 0s - loss: 0.2819 - accuracy: 0.8959"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1654/1875 [=========================>....] - ETA: 0s - loss: 0.2817 - accuracy: 0.8959"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1678/1875 [=========================>....] - ETA: 0s - loss: 0.2813 - accuracy: 0.8961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1701/1875 [==========================>...] - ETA: 0s - loss: 0.2813 - accuracy: 0.8961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1724/1875 [==========================>...] - ETA: 0s - loss: 0.2808 - accuracy: 0.8962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1747/1875 [==========================>...] - ETA: 0s - loss: 0.2808 - accuracy: 0.8962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1770/1875 [===========================>..] - ETA: 0s - loss: 0.2802 - accuracy: 0.8964"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1793/1875 [===========================>..] - ETA: 0s - loss: 0.2804 - accuracy: 0.8964"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1816/1875 [============================>.] - ETA: 0s - loss: 0.2803 - accuracy: 0.8965"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1839/1875 [============================>.] - ETA: 0s - loss: 0.2801 - accuracy: 0.8964"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1862/1875 [============================>.] - ETA: 0s - loss: 0.2805 - accuracy: 0.8963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2805 - accuracy: 0.8965\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 5s - loss: 0.3247 - accuracy: 0.8438"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  24/1875 [..............................] - ETA: 4s - loss: 0.2538 - accuracy: 0.9089"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  47/1875 [..............................] - ETA: 4s - loss: 0.2626 - accuracy: 0.9056"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  70/1875 [>.............................] - ETA: 4s - loss: 0.2674 - accuracy: 0.9031"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  93/1875 [>.............................] - ETA: 3s - loss: 0.2761 - accuracy: 0.9032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 116/1875 [>.............................] - ETA: 3s - loss: 0.2765 - accuracy: 0.9019"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 139/1875 [=>............................] - ETA: 3s - loss: 0.2727 - accuracy: 0.9045"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 161/1875 [=>............................] - ETA: 3s - loss: 0.2733 - accuracy: 0.9022"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 184/1875 [=>............................] - ETA: 3s - loss: 0.2736 - accuracy: 0.9015"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 206/1875 [==>...........................] - ETA: 3s - loss: 0.2727 - accuracy: 0.9019"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 229/1875 [==>...........................] - ETA: 3s - loss: 0.2709 - accuracy: 0.9023"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 252/1875 [===>..........................] - ETA: 3s - loss: 0.2721 - accuracy: 0.9019"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 275/1875 [===>..........................] - ETA: 3s - loss: 0.2700 - accuracy: 0.9033"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 298/1875 [===>..........................] - ETA: 3s - loss: 0.2699 - accuracy: 0.9032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 321/1875 [====>.........................] - ETA: 3s - loss: 0.2703 - accuracy: 0.9031"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 344/1875 [====>.........................] - ETA: 3s - loss: 0.2707 - accuracy: 0.9028"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 367/1875 [====>.........................] - ETA: 3s - loss: 0.2731 - accuracy: 0.9022"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 390/1875 [=====>........................] - ETA: 3s - loss: 0.2745 - accuracy: 0.9018"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 414/1875 [=====>........................] - ETA: 3s - loss: 0.2741 - accuracy: 0.9023"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 438/1875 [======>.......................] - ETA: 3s - loss: 0.2766 - accuracy: 0.9013"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 461/1875 [======>.......................] - ETA: 3s - loss: 0.2752 - accuracy: 0.9014"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 485/1875 [======>.......................] - ETA: 3s - loss: 0.2741 - accuracy: 0.9020"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 508/1875 [=======>......................] - ETA: 3s - loss: 0.2723 - accuracy: 0.9023"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 531/1875 [=======>......................] - ETA: 2s - loss: 0.2720 - accuracy: 0.9025"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 554/1875 [=======>......................] - ETA: 2s - loss: 0.2734 - accuracy: 0.9019"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 577/1875 [========>.....................] - ETA: 2s - loss: 0.2720 - accuracy: 0.9022"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 600/1875 [========>.....................] - ETA: 2s - loss: 0.2718 - accuracy: 0.9023"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 623/1875 [========>.....................] - ETA: 2s - loss: 0.2720 - accuracy: 0.9020"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 647/1875 [=========>....................] - ETA: 2s - loss: 0.2707 - accuracy: 0.9024"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 670/1875 [=========>....................] - ETA: 2s - loss: 0.2714 - accuracy: 0.9020"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 693/1875 [==========>...................] - ETA: 2s - loss: 0.2708 - accuracy: 0.9023"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 716/1875 [==========>...................] - ETA: 2s - loss: 0.2715 - accuracy: 0.9024"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 739/1875 [==========>...................] - ETA: 2s - loss: 0.2708 - accuracy: 0.9025"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 762/1875 [===========>..................] - ETA: 2s - loss: 0.2718 - accuracy: 0.9019"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 785/1875 [===========>..................] - ETA: 2s - loss: 0.2717 - accuracy: 0.9016"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 808/1875 [===========>..................] - ETA: 2s - loss: 0.2709 - accuracy: 0.9013"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 831/1875 [============>.................] - ETA: 2s - loss: 0.2711 - accuracy: 0.9012"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 854/1875 [============>.................] - ETA: 2s - loss: 0.2710 - accuracy: 0.9010"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 877/1875 [=============>................] - ETA: 2s - loss: 0.2703 - accuracy: 0.9010"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 900/1875 [=============>................] - ETA: 2s - loss: 0.2698 - accuracy: 0.9014"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 923/1875 [=============>................] - ETA: 2s - loss: 0.2698 - accuracy: 0.9011"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 947/1875 [==============>...............] - ETA: 2s - loss: 0.2699 - accuracy: 0.9014"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 971/1875 [==============>...............] - ETA: 1s - loss: 0.2695 - accuracy: 0.9012"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 995/1875 [==============>...............] - ETA: 1s - loss: 0.2690 - accuracy: 0.9014"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1018/1875 [===============>..............] - ETA: 1s - loss: 0.2695 - accuracy: 0.9012"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1042/1875 [===============>..............] - ETA: 1s - loss: 0.2689 - accuracy: 0.9015"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1065/1875 [================>.............] - ETA: 1s - loss: 0.2684 - accuracy: 0.9017"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1088/1875 [================>.............] - ETA: 1s - loss: 0.2693 - accuracy: 0.9014"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1111/1875 [================>.............] - ETA: 1s - loss: 0.2704 - accuracy: 0.9009"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1134/1875 [=================>............] - ETA: 1s - loss: 0.2702 - accuracy: 0.9010"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1157/1875 [=================>............] - ETA: 1s - loss: 0.2711 - accuracy: 0.9007"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1180/1875 [=================>............] - ETA: 1s - loss: 0.2711 - accuracy: 0.9005"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1203/1875 [==================>...........] - ETA: 1s - loss: 0.2712 - accuracy: 0.9002"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1226/1875 [==================>...........] - ETA: 1s - loss: 0.2704 - accuracy: 0.9005"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1249/1875 [==================>...........] - ETA: 1s - loss: 0.2699 - accuracy: 0.9007"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1272/1875 [===================>..........] - ETA: 1s - loss: 0.2690 - accuracy: 0.9011"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1295/1875 [===================>..........] - ETA: 1s - loss: 0.2695 - accuracy: 0.9010"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1318/1875 [====================>.........] - ETA: 1s - loss: 0.2690 - accuracy: 0.9011"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1341/1875 [====================>.........] - ETA: 1s - loss: 0.2690 - accuracy: 0.9010"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1365/1875 [====================>.........] - ETA: 1s - loss: 0.2697 - accuracy: 0.9008"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1389/1875 [=====================>........] - ETA: 1s - loss: 0.2700 - accuracy: 0.9007"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1412/1875 [=====================>........] - ETA: 1s - loss: 0.2704 - accuracy: 0.9007"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1435/1875 [=====================>........] - ETA: 0s - loss: 0.2705 - accuracy: 0.9006"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1458/1875 [======================>.......] - ETA: 0s - loss: 0.2702 - accuracy: 0.9009"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1481/1875 [======================>.......] - ETA: 0s - loss: 0.2704 - accuracy: 0.9006"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1504/1875 [=======================>......] - ETA: 0s - loss: 0.2706 - accuracy: 0.9006"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1527/1875 [=======================>......] - ETA: 0s - loss: 0.2703 - accuracy: 0.9008"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1550/1875 [=======================>......] - ETA: 0s - loss: 0.2705 - accuracy: 0.9007"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1573/1875 [========================>.....] - ETA: 0s - loss: 0.2709 - accuracy: 0.9007"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1596/1875 [========================>.....] - ETA: 0s - loss: 0.2713 - accuracy: 0.9004"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1620/1875 [========================>.....] - ETA: 0s - loss: 0.2713 - accuracy: 0.9003"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1644/1875 [=========================>....] - ETA: 0s - loss: 0.2704 - accuracy: 0.9007"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1668/1875 [=========================>....] - ETA: 0s - loss: 0.2701 - accuracy: 0.9008"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1691/1875 [==========================>...] - ETA: 0s - loss: 0.2705 - accuracy: 0.9006"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1714/1875 [==========================>...] - ETA: 0s - loss: 0.2705 - accuracy: 0.9006"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1737/1875 [==========================>...] - ETA: 0s - loss: 0.2702 - accuracy: 0.9007"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1760/1875 [===========================>..] - ETA: 0s - loss: 0.2697 - accuracy: 0.9009"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1783/1875 [===========================>..] - ETA: 0s - loss: 0.2694 - accuracy: 0.9009"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1805/1875 [===========================>..] - ETA: 0s - loss: 0.2697 - accuracy: 0.9008"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1827/1875 [============================>.] - ETA: 0s - loss: 0.2700 - accuracy: 0.9007"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1851/1875 [============================>.] - ETA: 0s - loss: 0.2703 - accuracy: 0.9008"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1874/1875 [============================>.] - ETA: 0s - loss: 0.2704 - accuracy: 0.9007"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2703 - accuracy: 0.9007\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 5s - loss: 0.3601 - accuracy: 0.8750"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  24/1875 [..............................] - ETA: 4s - loss: 0.2529 - accuracy: 0.9010"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  47/1875 [..............................] - ETA: 4s - loss: 0.2425 - accuracy: 0.9069"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  70/1875 [>.............................] - ETA: 4s - loss: 0.2405 - accuracy: 0.9089"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  93/1875 [>.............................] - ETA: 3s - loss: 0.2428 - accuracy: 0.9073"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 116/1875 [>.............................] - ETA: 3s - loss: 0.2469 - accuracy: 0.9046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 139/1875 [=>............................] - ETA: 3s - loss: 0.2480 - accuracy: 0.9051"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 162/1875 [=>............................] - ETA: 3s - loss: 0.2524 - accuracy: 0.9032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 185/1875 [=>............................] - ETA: 3s - loss: 0.2513 - accuracy: 0.9041"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 207/1875 [==>...........................] - ETA: 3s - loss: 0.2531 - accuracy: 0.9032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 230/1875 [==>...........................] - ETA: 3s - loss: 0.2511 - accuracy: 0.9034"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 253/1875 [===>..........................] - ETA: 3s - loss: 0.2541 - accuracy: 0.9030"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 276/1875 [===>..........................] - ETA: 3s - loss: 0.2540 - accuracy: 0.9023"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 299/1875 [===>..........................] - ETA: 3s - loss: 0.2538 - accuracy: 0.9024"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 322/1875 [====>.........................] - ETA: 3s - loss: 0.2521 - accuracy: 0.9029"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 345/1875 [====>.........................] - ETA: 3s - loss: 0.2536 - accuracy: 0.9023"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 367/1875 [====>.........................] - ETA: 3s - loss: 0.2548 - accuracy: 0.9021"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 389/1875 [=====>........................] - ETA: 3s - loss: 0.2544 - accuracy: 0.9023"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 411/1875 [=====>........................] - ETA: 3s - loss: 0.2508 - accuracy: 0.9039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 433/1875 [=====>........................] - ETA: 3s - loss: 0.2498 - accuracy: 0.9044"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 455/1875 [======>.......................] - ETA: 3s - loss: 0.2501 - accuracy: 0.9047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 477/1875 [======>.......................] - ETA: 3s - loss: 0.2485 - accuracy: 0.9051"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 499/1875 [======>.......................] - ETA: 3s - loss: 0.2481 - accuracy: 0.9052"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 522/1875 [=======>......................] - ETA: 3s - loss: 0.2474 - accuracy: 0.9060"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 544/1875 [=======>......................] - ETA: 3s - loss: 0.2470 - accuracy: 0.9062"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 567/1875 [========>.....................] - ETA: 2s - loss: 0.2474 - accuracy: 0.9059"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 590/1875 [========>.....................] - ETA: 2s - loss: 0.2469 - accuracy: 0.9065"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 612/1875 [========>.....................] - ETA: 2s - loss: 0.2479 - accuracy: 0.9059"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 635/1875 [=========>....................] - ETA: 2s - loss: 0.2493 - accuracy: 0.9050"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 657/1875 [=========>....................] - ETA: 2s - loss: 0.2501 - accuracy: 0.9050"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 679/1875 [=========>....................] - ETA: 2s - loss: 0.2507 - accuracy: 0.9048"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 701/1875 [==========>...................] - ETA: 2s - loss: 0.2496 - accuracy: 0.9054"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 724/1875 [==========>...................] - ETA: 2s - loss: 0.2505 - accuracy: 0.9046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 746/1875 [==========>...................] - ETA: 2s - loss: 0.2523 - accuracy: 0.9038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 769/1875 [===========>..................] - ETA: 2s - loss: 0.2526 - accuracy: 0.9034"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 791/1875 [===========>..................] - ETA: 2s - loss: 0.2524 - accuracy: 0.9040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 813/1875 [============>.................] - ETA: 2s - loss: 0.2528 - accuracy: 0.9039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 835/1875 [============>.................] - ETA: 2s - loss: 0.2536 - accuracy: 0.9037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 857/1875 [============>.................] - ETA: 2s - loss: 0.2529 - accuracy: 0.9040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 879/1875 [=============>................] - ETA: 2s - loss: 0.2528 - accuracy: 0.9041"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 901/1875 [=============>................] - ETA: 2s - loss: 0.2537 - accuracy: 0.9040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 924/1875 [=============>................] - ETA: 2s - loss: 0.2545 - accuracy: 0.9039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 947/1875 [==============>...............] - ETA: 2s - loss: 0.2546 - accuracy: 0.9038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 970/1875 [==============>...............] - ETA: 2s - loss: 0.2550 - accuracy: 0.9037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 992/1875 [==============>...............] - ETA: 2s - loss: 0.2547 - accuracy: 0.9041"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1015/1875 [===============>..............] - ETA: 1s - loss: 0.2554 - accuracy: 0.9039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1037/1875 [===============>..............] - ETA: 1s - loss: 0.2551 - accuracy: 0.9042"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1059/1875 [===============>..............] - ETA: 1s - loss: 0.2549 - accuracy: 0.9042"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1081/1875 [================>.............] - ETA: 1s - loss: 0.2550 - accuracy: 0.9043"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1104/1875 [================>.............] - ETA: 1s - loss: 0.2561 - accuracy: 0.9038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1126/1875 [=================>............] - ETA: 1s - loss: 0.2559 - accuracy: 0.9037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1148/1875 [=================>............] - ETA: 1s - loss: 0.2567 - accuracy: 0.9036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1172/1875 [=================>............] - ETA: 1s - loss: 0.2567 - accuracy: 0.9036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1195/1875 [==================>...........] - ETA: 1s - loss: 0.2559 - accuracy: 0.9038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1219/1875 [==================>...........] - ETA: 1s - loss: 0.2564 - accuracy: 0.9037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1243/1875 [==================>...........] - ETA: 1s - loss: 0.2572 - accuracy: 0.9032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1266/1875 [===================>..........] - ETA: 1s - loss: 0.2570 - accuracy: 0.9033"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1289/1875 [===================>..........] - ETA: 1s - loss: 0.2574 - accuracy: 0.9032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1313/1875 [====================>.........] - ETA: 1s - loss: 0.2578 - accuracy: 0.9029"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1337/1875 [====================>.........] - ETA: 1s - loss: 0.2575 - accuracy: 0.9028"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1360/1875 [====================>.........] - ETA: 1s - loss: 0.2572 - accuracy: 0.9031"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1382/1875 [=====================>........] - ETA: 1s - loss: 0.2574 - accuracy: 0.9031"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1404/1875 [=====================>........] - ETA: 1s - loss: 0.2574 - accuracy: 0.9033"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1426/1875 [=====================>........] - ETA: 1s - loss: 0.2571 - accuracy: 0.9034"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1448/1875 [======================>.......] - ETA: 0s - loss: 0.2575 - accuracy: 0.9031"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1470/1875 [======================>.......] - ETA: 0s - loss: 0.2576 - accuracy: 0.9029"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1492/1875 [======================>.......] - ETA: 0s - loss: 0.2581 - accuracy: 0.9028"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1514/1875 [=======================>......] - ETA: 0s - loss: 0.2584 - accuracy: 0.9027"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1537/1875 [=======================>......] - ETA: 0s - loss: 0.2581 - accuracy: 0.9029"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1559/1875 [=======================>......] - ETA: 0s - loss: 0.2584 - accuracy: 0.9029"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1582/1875 [========================>.....] - ETA: 0s - loss: 0.2582 - accuracy: 0.9031"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1605/1875 [========================>.....] - ETA: 0s - loss: 0.2581 - accuracy: 0.9032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1628/1875 [=========================>....] - ETA: 0s - loss: 0.2582 - accuracy: 0.9032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1651/1875 [=========================>....] - ETA: 0s - loss: 0.2579 - accuracy: 0.9032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1675/1875 [=========================>....] - ETA: 0s - loss: 0.2581 - accuracy: 0.9032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1699/1875 [==========================>...] - ETA: 0s - loss: 0.2576 - accuracy: 0.9033"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1722/1875 [==========================>...] - ETA: 0s - loss: 0.2576 - accuracy: 0.9032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1745/1875 [==========================>...] - ETA: 0s - loss: 0.2579 - accuracy: 0.9031"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1767/1875 [===========================>..] - ETA: 0s - loss: 0.2582 - accuracy: 0.9032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1789/1875 [===========================>..] - ETA: 0s - loss: 0.2584 - accuracy: 0.9031"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1811/1875 [===========================>..] - ETA: 0s - loss: 0.2590 - accuracy: 0.9031"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1834/1875 [============================>.] - ETA: 0s - loss: 0.2594 - accuracy: 0.9029"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1857/1875 [============================>.] - ETA: 0s - loss: 0.2595 - accuracy: 0.9030"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2594 - accuracy: 0.9030\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 5s - loss: 0.2921 - accuracy: 0.8750"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  24/1875 [..............................] - ETA: 4s - loss: 0.2275 - accuracy: 0.9010"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  47/1875 [..............................] - ETA: 4s - loss: 0.2355 - accuracy: 0.9036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  69/1875 [>.............................] - ETA: 4s - loss: 0.2320 - accuracy: 0.9081"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  92/1875 [>.............................] - ETA: 4s - loss: 0.2243 - accuracy: 0.9120"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 115/1875 [>.............................] - ETA: 3s - loss: 0.2297 - accuracy: 0.9090"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 138/1875 [=>............................] - ETA: 3s - loss: 0.2274 - accuracy: 0.9110"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 161/1875 [=>............................] - ETA: 3s - loss: 0.2283 - accuracy: 0.9113"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 185/1875 [=>............................] - ETA: 3s - loss: 0.2318 - accuracy: 0.9088"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 208/1875 [==>...........................] - ETA: 3s - loss: 0.2294 - accuracy: 0.9103"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 231/1875 [==>...........................] - ETA: 3s - loss: 0.2322 - accuracy: 0.9103"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 254/1875 [===>..........................] - ETA: 3s - loss: 0.2363 - accuracy: 0.9092"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 277/1875 [===>..........................] - ETA: 3s - loss: 0.2373 - accuracy: 0.9082"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 300/1875 [===>..........................] - ETA: 3s - loss: 0.2365 - accuracy: 0.9081"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 323/1875 [====>.........................] - ETA: 3s - loss: 0.2402 - accuracy: 0.9066"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 345/1875 [====>.........................] - ETA: 3s - loss: 0.2413 - accuracy: 0.9062"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 367/1875 [====>.........................] - ETA: 3s - loss: 0.2423 - accuracy: 0.9062"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 390/1875 [=====>........................] - ETA: 3s - loss: 0.2426 - accuracy: 0.9067"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 413/1875 [=====>........................] - ETA: 3s - loss: 0.2429 - accuracy: 0.9069"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 436/1875 [=====>........................] - ETA: 3s - loss: 0.2421 - accuracy: 0.9073"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 459/1875 [======>.......................] - ETA: 3s - loss: 0.2427 - accuracy: 0.9075"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 482/1875 [======>.......................] - ETA: 3s - loss: 0.2424 - accuracy: 0.9078"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 504/1875 [=======>......................] - ETA: 3s - loss: 0.2428 - accuracy: 0.9078"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 527/1875 [=======>......................] - ETA: 3s - loss: 0.2435 - accuracy: 0.9074"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 550/1875 [=======>......................] - ETA: 2s - loss: 0.2440 - accuracy: 0.9072"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 573/1875 [========>.....................] - ETA: 2s - loss: 0.2438 - accuracy: 0.9075"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 596/1875 [========>.....................] - ETA: 2s - loss: 0.2457 - accuracy: 0.9071"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 619/1875 [========>.....................] - ETA: 2s - loss: 0.2459 - accuracy: 0.9075"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 642/1875 [=========>....................] - ETA: 2s - loss: 0.2451 - accuracy: 0.9079"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 665/1875 [=========>....................] - ETA: 2s - loss: 0.2455 - accuracy: 0.9076"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 687/1875 [=========>....................] - ETA: 2s - loss: 0.2457 - accuracy: 0.9071"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 710/1875 [==========>...................] - ETA: 2s - loss: 0.2457 - accuracy: 0.9073"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 732/1875 [==========>...................] - ETA: 2s - loss: 0.2465 - accuracy: 0.9070"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 755/1875 [===========>..................] - ETA: 2s - loss: 0.2461 - accuracy: 0.9073"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 777/1875 [===========>..................] - ETA: 2s - loss: 0.2476 - accuracy: 0.9071"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 800/1875 [===========>..................] - ETA: 2s - loss: 0.2477 - accuracy: 0.9070"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 823/1875 [============>.................] - ETA: 2s - loss: 0.2477 - accuracy: 0.9073"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 846/1875 [============>.................] - ETA: 2s - loss: 0.2476 - accuracy: 0.9075"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 869/1875 [============>.................] - ETA: 2s - loss: 0.2482 - accuracy: 0.9075"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 892/1875 [=============>................] - ETA: 2s - loss: 0.2482 - accuracy: 0.9074"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 915/1875 [=============>................] - ETA: 2s - loss: 0.2479 - accuracy: 0.9071"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 938/1875 [==============>...............] - ETA: 2s - loss: 0.2483 - accuracy: 0.9069"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 961/1875 [==============>...............] - ETA: 2s - loss: 0.2473 - accuracy: 0.9073"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 983/1875 [==============>...............] - ETA: 2s - loss: 0.2474 - accuracy: 0.9072"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1005/1875 [===============>..............] - ETA: 1s - loss: 0.2483 - accuracy: 0.9067"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1027/1875 [===============>..............] - ETA: 1s - loss: 0.2483 - accuracy: 0.9066"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1050/1875 [===============>..............] - ETA: 1s - loss: 0.2499 - accuracy: 0.9060"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1073/1875 [================>.............] - ETA: 1s - loss: 0.2501 - accuracy: 0.9060"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1096/1875 [================>.............] - ETA: 1s - loss: 0.2503 - accuracy: 0.9059"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1119/1875 [================>.............] - ETA: 1s - loss: 0.2496 - accuracy: 0.9061"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1142/1875 [=================>............] - ETA: 1s - loss: 0.2498 - accuracy: 0.9062"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1165/1875 [=================>............] - ETA: 1s - loss: 0.2491 - accuracy: 0.9065"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1188/1875 [==================>...........] - ETA: 1s - loss: 0.2484 - accuracy: 0.9066"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1211/1875 [==================>...........] - ETA: 1s - loss: 0.2479 - accuracy: 0.9067"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1234/1875 [==================>...........] - ETA: 1s - loss: 0.2501 - accuracy: 0.9059"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1257/1875 [===================>..........] - ETA: 1s - loss: 0.2507 - accuracy: 0.9056"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1280/1875 [===================>..........] - ETA: 1s - loss: 0.2510 - accuracy: 0.9056"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1303/1875 [===================>..........] - ETA: 1s - loss: 0.2508 - accuracy: 0.9056"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1326/1875 [====================>.........] - ETA: 1s - loss: 0.2498 - accuracy: 0.9061"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1349/1875 [====================>.........] - ETA: 1s - loss: 0.2495 - accuracy: 0.9062"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1372/1875 [====================>.........] - ETA: 1s - loss: 0.2498 - accuracy: 0.9060"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1395/1875 [=====================>........] - ETA: 1s - loss: 0.2494 - accuracy: 0.9058"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1418/1875 [=====================>........] - ETA: 1s - loss: 0.2494 - accuracy: 0.9057"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1440/1875 [======================>.......] - ETA: 0s - loss: 0.2495 - accuracy: 0.9059"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1463/1875 [======================>.......] - ETA: 0s - loss: 0.2495 - accuracy: 0.9058"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1486/1875 [======================>.......] - ETA: 0s - loss: 0.2498 - accuracy: 0.9055"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1508/1875 [=======================>......] - ETA: 0s - loss: 0.2503 - accuracy: 0.9052"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1531/1875 [=======================>......] - ETA: 0s - loss: 0.2502 - accuracy: 0.9054"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1554/1875 [=======================>......] - ETA: 0s - loss: 0.2508 - accuracy: 0.9052"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1577/1875 [========================>.....] - ETA: 0s - loss: 0.2511 - accuracy: 0.9051"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1599/1875 [========================>.....] - ETA: 0s - loss: 0.2514 - accuracy: 0.9050"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1622/1875 [========================>.....] - ETA: 0s - loss: 0.2512 - accuracy: 0.9052"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1645/1875 [=========================>....] - ETA: 0s - loss: 0.2513 - accuracy: 0.9054"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1667/1875 [=========================>....] - ETA: 0s - loss: 0.2513 - accuracy: 0.9053"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1689/1875 [==========================>...] - ETA: 0s - loss: 0.2513 - accuracy: 0.9053"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1712/1875 [==========================>...] - ETA: 0s - loss: 0.2511 - accuracy: 0.9054"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1736/1875 [==========================>...] - ETA: 0s - loss: 0.2512 - accuracy: 0.9053"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1759/1875 [===========================>..] - ETA: 0s - loss: 0.2503 - accuracy: 0.9058"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1781/1875 [===========================>..] - ETA: 0s - loss: 0.2504 - accuracy: 0.9057"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1804/1875 [===========================>..] - ETA: 0s - loss: 0.2506 - accuracy: 0.9057"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1827/1875 [============================>.] - ETA: 0s - loss: 0.2505 - accuracy: 0.9059"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1850/1875 [============================>.] - ETA: 0s - loss: 0.2507 - accuracy: 0.9059"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1873/1875 [============================>.] - ETA: 0s - loss: 0.2507 - accuracy: 0.9059"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2507 - accuracy: 0.9059\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "   1/1875 [..............................] - ETA: 5s - loss: 0.0879 - accuracy: 0.9688"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  23/1875 [..............................] - ETA: 4s - loss: 0.2670 - accuracy: 0.9103"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  45/1875 [..............................] - ETA: 4s - loss: 0.2402 - accuracy: 0.9167"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  68/1875 [>.............................] - ETA: 4s - loss: 0.2591 - accuracy: 0.9131"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  90/1875 [>.............................] - ETA: 4s - loss: 0.2507 - accuracy: 0.9104"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 113/1875 [>.............................] - ETA: 4s - loss: 0.2524 - accuracy: 0.9093"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 136/1875 [=>............................] - ETA: 3s - loss: 0.2520 - accuracy: 0.9099"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 158/1875 [=>............................] - ETA: 3s - loss: 0.2471 - accuracy: 0.9116"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 181/1875 [=>............................] - ETA: 3s - loss: 0.2462 - accuracy: 0.9123"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 204/1875 [==>...........................] - ETA: 3s - loss: 0.2468 - accuracy: 0.9125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 227/1875 [==>...........................] - ETA: 3s - loss: 0.2449 - accuracy: 0.9130"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 250/1875 [===>..........................] - ETA: 3s - loss: 0.2479 - accuracy: 0.9122"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 273/1875 [===>..........................] - ETA: 3s - loss: 0.2470 - accuracy: 0.9127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 296/1875 [===>..........................] - ETA: 3s - loss: 0.2458 - accuracy: 0.9129"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 319/1875 [====>.........................] - ETA: 3s - loss: 0.2456 - accuracy: 0.9116"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 342/1875 [====>.........................] - ETA: 3s - loss: 0.2446 - accuracy: 0.9105"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 365/1875 [====>.........................] - ETA: 3s - loss: 0.2433 - accuracy: 0.9105"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 388/1875 [=====>........................] - ETA: 3s - loss: 0.2401 - accuracy: 0.9121"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 411/1875 [=====>........................] - ETA: 3s - loss: 0.2383 - accuracy: 0.9132"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 434/1875 [=====>........................] - ETA: 3s - loss: 0.2404 - accuracy: 0.9127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 457/1875 [======>.......................] - ETA: 3s - loss: 0.2426 - accuracy: 0.9121"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 480/1875 [======>.......................] - ETA: 3s - loss: 0.2419 - accuracy: 0.9122"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 503/1875 [=======>......................] - ETA: 3s - loss: 0.2412 - accuracy: 0.9123"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 526/1875 [=======>......................] - ETA: 3s - loss: 0.2431 - accuracy: 0.9118"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 549/1875 [=======>......................] - ETA: 2s - loss: 0.2434 - accuracy: 0.9117"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 572/1875 [========>.....................] - ETA: 2s - loss: 0.2435 - accuracy: 0.9113"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 595/1875 [========>.....................] - ETA: 2s - loss: 0.2425 - accuracy: 0.9118"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 618/1875 [========>.....................] - ETA: 2s - loss: 0.2438 - accuracy: 0.9114"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 640/1875 [=========>....................] - ETA: 2s - loss: 0.2435 - accuracy: 0.9111"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 663/1875 [=========>....................] - ETA: 2s - loss: 0.2446 - accuracy: 0.9106"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 686/1875 [=========>....................] - ETA: 2s - loss: 0.2450 - accuracy: 0.9104"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 709/1875 [==========>...................] - ETA: 2s - loss: 0.2444 - accuracy: 0.9100"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 732/1875 [==========>...................] - ETA: 2s - loss: 0.2443 - accuracy: 0.9101"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 755/1875 [===========>..................] - ETA: 2s - loss: 0.2434 - accuracy: 0.9101"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 778/1875 [===========>..................] - ETA: 2s - loss: 0.2431 - accuracy: 0.9097"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 801/1875 [===========>..................] - ETA: 2s - loss: 0.2432 - accuracy: 0.9096"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 824/1875 [============>.................] - ETA: 2s - loss: 0.2427 - accuracy: 0.9097"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 847/1875 [============>.................] - ETA: 2s - loss: 0.2437 - accuracy: 0.9093"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 870/1875 [============>.................] - ETA: 2s - loss: 0.2438 - accuracy: 0.9092"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 893/1875 [=============>................] - ETA: 2s - loss: 0.2420 - accuracy: 0.9100"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 916/1875 [=============>................] - ETA: 2s - loss: 0.2422 - accuracy: 0.9101"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 938/1875 [==============>...............] - ETA: 2s - loss: 0.2421 - accuracy: 0.9101"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 961/1875 [==============>...............] - ETA: 2s - loss: 0.2419 - accuracy: 0.9101"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 984/1875 [==============>...............] - ETA: 1s - loss: 0.2412 - accuracy: 0.9103"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1007/1875 [===============>..............] - ETA: 1s - loss: 0.2414 - accuracy: 0.9103"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1030/1875 [===============>..............] - ETA: 1s - loss: 0.2407 - accuracy: 0.9104"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1053/1875 [===============>..............] - ETA: 1s - loss: 0.2408 - accuracy: 0.9104"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1076/1875 [================>.............] - ETA: 1s - loss: 0.2406 - accuracy: 0.9103"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1098/1875 [================>.............] - ETA: 1s - loss: 0.2404 - accuracy: 0.9104"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1122/1875 [================>.............] - ETA: 1s - loss: 0.2404 - accuracy: 0.9103"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1145/1875 [=================>............] - ETA: 1s - loss: 0.2396 - accuracy: 0.9106"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1168/1875 [=================>............] - ETA: 1s - loss: 0.2394 - accuracy: 0.9109"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1191/1875 [==================>...........] - ETA: 1s - loss: 0.2394 - accuracy: 0.9108"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1214/1875 [==================>...........] - ETA: 1s - loss: 0.2394 - accuracy: 0.9109"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1238/1875 [==================>...........] - ETA: 1s - loss: 0.2395 - accuracy: 0.9110"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1261/1875 [===================>..........] - ETA: 1s - loss: 0.2401 - accuracy: 0.9110"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1284/1875 [===================>..........] - ETA: 1s - loss: 0.2403 - accuracy: 0.9107"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1307/1875 [===================>..........] - ETA: 1s - loss: 0.2412 - accuracy: 0.9105"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1330/1875 [====================>.........] - ETA: 1s - loss: 0.2412 - accuracy: 0.9105"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1353/1875 [====================>.........] - ETA: 1s - loss: 0.2416 - accuracy: 0.9105"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1376/1875 [=====================>........] - ETA: 1s - loss: 0.2413 - accuracy: 0.9106"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1399/1875 [=====================>........] - ETA: 1s - loss: 0.2411 - accuracy: 0.9106"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1422/1875 [=====================>........] - ETA: 1s - loss: 0.2408 - accuracy: 0.9108"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1445/1875 [======================>.......] - ETA: 0s - loss: 0.2409 - accuracy: 0.9109"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1468/1875 [======================>.......] - ETA: 0s - loss: 0.2409 - accuracy: 0.9109"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1492/1875 [======================>.......] - ETA: 0s - loss: 0.2404 - accuracy: 0.9111"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1516/1875 [=======================>......] - ETA: 0s - loss: 0.2405 - accuracy: 0.9111"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1539/1875 [=======================>......] - ETA: 0s - loss: 0.2414 - accuracy: 0.9107"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1562/1875 [=======================>......] - ETA: 0s - loss: 0.2412 - accuracy: 0.9106"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1585/1875 [========================>.....] - ETA: 0s - loss: 0.2414 - accuracy: 0.9105"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1608/1875 [========================>.....] - ETA: 0s - loss: 0.2417 - accuracy: 0.9103"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1631/1875 [=========================>....] - ETA: 0s - loss: 0.2421 - accuracy: 0.9101"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1653/1875 [=========================>....] - ETA: 0s - loss: 0.2420 - accuracy: 0.9102"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1676/1875 [=========================>....] - ETA: 0s - loss: 0.2420 - accuracy: 0.9103"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1698/1875 [==========================>...] - ETA: 0s - loss: 0.2417 - accuracy: 0.9104"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1721/1875 [==========================>...] - ETA: 0s - loss: 0.2410 - accuracy: 0.9107"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1744/1875 [==========================>...] - ETA: 0s - loss: 0.2410 - accuracy: 0.9107"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1767/1875 [===========================>..] - ETA: 0s - loss: 0.2409 - accuracy: 0.9107"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1790/1875 [===========================>..] - ETA: 0s - loss: 0.2412 - accuracy: 0.9108"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1813/1875 [============================>.] - ETA: 0s - loss: 0.2411 - accuracy: 0.9107"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1836/1875 [============================>.] - ETA: 0s - loss: 0.2410 - accuracy: 0.9108"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1859/1875 [============================>.] - ETA: 0s - loss: 0.2414 - accuracy: 0.9106"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2418 - accuracy: 0.9104\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x7f48f80d1b20>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W3ZVOhugCaXA"
   },
   "source": [
    "在模型训练期间，会显示损失和准确率指标。此模型在训练数据上的准确率达到了 0.91（或 91%）左右。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wCpr6DGyE28h"
   },
   "source": [
    "### 评估准确率\n",
    "\n",
    "接下来，比较模型在测试数据集上的表现："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:48.330553Z",
     "iopub.status.busy": "2023-11-08T00:32:48.330234Z",
     "iopub.status.idle": "2023-11-08T00:32:49.146329Z",
     "shell.execute_reply": "2023-11-08T00:32:49.145520Z"
    },
    "id": "VflXLEeECaXC"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 1s - loss: 0.3330 - accuracy: 0.8824 - 654ms/epoch - 2ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test accuracy: 0.8823999762535095\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
    "\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yWfgsmVXCaXG"
   },
   "source": [
    "结果表明，模型在测试数据集上的准确率略低于训练数据集。训练准确率和测试准确率之间的差距代表*过拟合*。过拟合是指机器学习模型在新的、以前未曾见过的输入上的表现不如在训练数据上的表现。过拟合的模型会“记住”训练数据集中的噪声和细节，从而对模型在新数据上的表现产生负面影响。有关更多信息，请参阅以下内容：\n",
    "\n",
    "- [演示过拟合](https://tensorflow.google.cn/tutorials/keras/overfit_and_underfit#demonstrate_overfitting)\n",
    "- [防止过拟合的策略](https://tensorflow.google.cn/tutorials/keras/overfit_and_underfit#strategies_to_prevent_overfitting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v-PyD1SYE28q"
   },
   "source": [
    "### 进行预测\n",
    "\n",
    "模型经过训练后，您可以使用它对一些图像进行预测。附加一个 Softmax 层，将模型的线性输出 [logits](https://developers.google.com/machine-learning/glossary#logits) 转换成更容易理解的概率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:49.150544Z",
     "iopub.status.busy": "2023-11-08T00:32:49.149962Z",
     "iopub.status.idle": "2023-11-08T00:32:49.173240Z",
     "shell.execute_reply": "2023-11-08T00:32:49.172536Z"
    },
    "id": "DnfNA0CrQLSD"
   },
   "outputs": [],
   "source": [
    "probability_model = tf.keras.Sequential([model, \n",
    "                                         tf.keras.layers.Softmax()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:49.176902Z",
     "iopub.status.busy": "2023-11-08T00:32:49.176626Z",
     "iopub.status.idle": "2023-11-08T00:32:49.985234Z",
     "shell.execute_reply": "2023-11-08T00:32:49.984263Z"
    },
    "id": "Gl91RPhdCaXI"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/313 [..............................] - ETA: 32s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 38/313 [==>...........................] - ETA: 0s "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 79/313 [======>.......................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "119/313 [==========>...................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "159/313 [==============>...............] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "200/313 [==================>...........] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "239/313 [=====================>........] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "279/313 [=========================>....] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "313/313 [==============================] - 1s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions = probability_model.predict(test_images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x9Kk1voUCaXJ"
   },
   "source": [
    "在上例中，模型预测了测试集中每个图像的标签。我们来看看第一个预测结果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:49.989969Z",
     "iopub.status.busy": "2023-11-08T00:32:49.989372Z",
     "iopub.status.idle": "2023-11-08T00:32:49.995194Z",
     "shell.execute_reply": "2023-11-08T00:32:49.994464Z"
    },
    "id": "3DmJEUinCaXK"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.46038394e-06, 1.75599275e-11, 1.09122176e-07, 5.28692201e-10,\n",
       "       5.17602139e-09, 1.99839560e-04, 7.88855459e-07, 1.64548866e-03,\n",
       "       5.95367396e-07, 9.98151720e-01], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-hw1hgeSCaXN"
   },
   "source": [
    "预测结果是一个包含 10 个数字的数组。它们代表模型对 10 种不同服装中每种服装的“置信度”。您可以看到哪个标签的置信度值最大："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:49.998786Z",
     "iopub.status.busy": "2023-11-08T00:32:49.998515Z",
     "iopub.status.idle": "2023-11-08T00:32:50.003623Z",
     "shell.execute_reply": "2023-11-08T00:32:50.002926Z"
    },
    "id": "qsqenuPnCaXO"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E51yS7iCCaXO"
   },
   "source": [
    "因此，该模型非常确信这个图像是短靴，或 `class_names[9]`。通过检查测试标签发现这个分类是正确的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:50.006956Z",
     "iopub.status.busy": "2023-11-08T00:32:50.006684Z",
     "iopub.status.idle": "2023-11-08T00:32:50.011485Z",
     "shell.execute_reply": "2023-11-08T00:32:50.010792Z"
    },
    "id": "Sd7Pgsu6CaXP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ygh2yYC972ne"
   },
   "source": [
    "您可以将其绘制成图表，看看模型对于全部 10 个类的预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:50.014800Z",
     "iopub.status.busy": "2023-11-08T00:32:50.014540Z",
     "iopub.status.idle": "2023-11-08T00:32:50.021354Z",
     "shell.execute_reply": "2023-11-08T00:32:50.020627Z"
    },
    "id": "DvYmmrpIy6Y1"
   },
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  true_label, img = true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'blue'\n",
    "  else:\n",
    "    color = 'red'\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  true_label = true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks(range(10))\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zh9yABaME29S"
   },
   "source": [
    "### 验证预测结果\n",
    "\n",
    "在模型经过训练后，您可以使用它对一些图像进行预测。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d4Ov9OFDMmOD"
   },
   "source": [
    "我们来看看第 0 个图像、预测结果和预测数组。正确的预测标签为蓝色，错误的预测标签为红色。数字表示预测标签的百分比（总计为 100）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:50.025340Z",
     "iopub.status.busy": "2023-11-08T00:32:50.024642Z",
     "iopub.status.idle": "2023-11-08T00:32:50.147358Z",
     "shell.execute_reply": "2023-11-08T00:32:50.146601Z"
    },
    "id": "HV5jw-5HwSmO"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:50.150818Z",
     "iopub.status.busy": "2023-11-08T00:32:50.150540Z",
     "iopub.status.idle": "2023-11-08T00:32:50.276300Z",
     "shell.execute_reply": "2023-11-08T00:32:50.275469Z"
    },
    "id": "Ko-uzOufSCSe"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAESCAYAAAAsZab9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/SrBM8AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAdg0lEQVR4nO3deXhUVZ7G8TcEAgEStrAkkAQSFRCEYREG0NYRFRmGxuVBdOARBbtHO06DjEyjdg9t2ywyo6PtgqIOTis04IILiogo4AKIYJTIDiooCKZZEsKe/OaPM2URSN1KKqFzhO/nefKQqnPPqVM3IW+de++5J87MTAAAoFrVqO4OAAAAAhkAAC8QyAAAeIBABgDAAwQyAAAeIJABAPAAgQwAgAdqVncHAPippKREO3bsUFJSkuLi4qq7O8BPlpmpsLBQaWlpqlEj8jiYQAZQph07dig9Pb26uwGcMbZv365WrVpFLCeQAZQpKSlJkvsjkpycXM29AX66CgoKlJ6e/uP/qUgIZABlCh2mTk5OJpCBKhDt1A8XdQEA4AECGQAADxDIAAB4gEAGAMADBDIAAB4gkAEA8EC5pj1xxx6gapT3jj0Azj7lCmTu2ANUrWh37AFw9ilXIHPHHqBqlPeOPQDOPuUKZO7YA1QtTv0AOBknsQAA8ACBDACABwhkAAA8QCADAOABAhkAAA8QyAAAeIBABgDAAwQyAAAeIJABAPAAgQwAgAcIZAAAPEAgAwDgAQIZAAAPEMgAAHiAQAYAwAMEMgAAHiCQAQDwAIEMAIAHCGQAADxAIAMA4AECGQAADxDIAAB4gEAGAMADBDIAAB4gkAEA8ACBDACABwhkAAA8QCADAOABAhkAAA8QyAAAeIBABgDAAwQyAAAeIJABAPAAgQwAgAcIZAAAPEAgAwDgAQIZAAAPEMgAAHiAQAYAwAMEMgAAHiCQAQDwAIEMAIAHCGQAADxAIAMA4AECGQAADxDIAAB4gEAGAMADBDIAAB4gkAEA8ACBDACABwhkAAA8QCADAOABAhkAAA8QyAAAeIBABgDAAwQyAAAeIJABAPAAgQwAgAcIZAAAPEAgAwDgAQIZAAAPEMgAAHiAQAYAwAMEMgAAHqhZ3R04Gz3xxBOB5Xl5eTHXrQwzCyyPi4s7ba8NAGc7RsgAAHiAQAYAwAMcsgaAM8G2bVJ+fuz1U1KkjIyq6w8qjEAGgJ+6bduktm2lw4djb6NOHWnDBkK5GnHIGgB+6vLzKxfGkqtfmRE2Ko1ABgDAAwQyAAAeqLJzyIcOHYpYlpiYeFralaSEhISY244mPj4+5rrz5s2LWLZjx47Aus2aNYtYdtNNNwXWnTBhQmB5enp6xLLKzDMuLi6OuW5l9jMAnCkYIQMA4AECGQAADxDIAAB4gEAGAMADBDIAAB4gkAEA8ECVTXsKmo5zxx13BNa95JJLIpZVZspUdQpaJrFHjx6BdYOmcrVq1Sqw7uzZswPLg6ZUXXPNNYF1k5KSIpZFm7oUNC0q2rKPpxNLSgLwBSNkAAA8QCADAOABAhkAAA8QyAAAeIBABgDAAwQyAAAeIJABAPBAheYhFxUVRZxv+u2330as9/rrrwe2e/DgwYhlHTt2DKzbuHHjiGV169YNrFtSUhJYvm3btohl06dPD6zbokWLiGUpKSmBdd94442IZYMGDQqsu2/fvsDyt956K2LZ+vXrA+tmZWVFLLviiisC62ZmZgaWny7RloUM+h2oUSP48yrLRgKoSoyQAQDwAIEMAIAHCGQAADxAIAMA4AECGQAADxDIAAB4oELTntatW6d69epV+EWKiooCy2fMmBGxrFOnToF1g5YqDCqTpM2bNweWr1mzJmLZ0aNHA+tefPHFEctWr14dWLdfv34Ry6JN5Yr2nq+66qqIZbt37w6su3Hjxohly5YtC6zbvn37iGUdOnQIrNu9e/fA8qZNm0YsizY1ialLAHzBCBkAAA8QyAAAeIBABgDAAwQyAAAeIJABAPAAgQwAgAcIZAAAPFChecj79u3TsWPHyizbs2dP5BepGfwy+/fvj1g2d+7cwLqNGjWKWBapryFJSUmB5b169YpYdt555wXWDVq6L9qSkvn5+RHLgpaqlIKXo5SCf07R5jhnZGTEVCZJBQUFEcs++OCDwLorV64MLA/qd8OGDQPrBi0L2axZs8C67dq1i1hWu3btwLoAcDJGyAAAeIBABgDAAwQyAAAeIJABAPAAgQwAgAcIZAAAPEAgAwDggQrNQ65Xr17E9ZCD1sq95ZZbAttt3bp1xLKgebOSdPjw4Yhl0eag1qlTJ+a2v/jii8C6QerXrx9YHjSvNto6zN9//31gedB6ycnJyYF1g147aJ6xJKWkpEQsizZ3Opqgn1O0NZ537NgRsSzavOw//vGPEcuGDRtW5vPR5pEDOHsxQgYAwAMEMgAAHiCQAQDwAIEMAIAHCGQAADxAIAMA4IEKTXt66623Ii4rl5qaGrFe0NQSKXjKTFZWVmDdoGX/jh8/Hlg3Wr+OHDkSsay4uDiwbpB9+/YFlgctR1mrVq3AutGWDKzMtKcg0aYINW/ePGJZtH0ZbapX0PS1aFPbgn73ov1+xMXFRSx76KGHynw+2u8kgLMXI2QAADxAIAMA4AECGQAADxDIAAB4gEAGAMADBDIAAB4gkAEA8ECF5iFv3bo14jzY7OzsiPU6duwY2G5eXl7Esm+//TawbmWWzyspKQksr0zdoLmz0ebVBs1vjTQPPOSHH34ILA+qn5iYGFg32hzoIPn5+RHLou3LwsLCwPKged3R6gYthRm0rKMkbdq0qcJ9qszvHIAzGyNkAAA8QCADAOABAhkAAA8QyAAAeIBABgDAAwQyAAAeqNC0p7p160ac+rJ8+fKI9SqzfF60ugcPHoxYFm05wZSUlMDyAwcORCyrzPKL8fHxgeU1a0b+sQSVSVKNGsGfsYKWX4wmaNpT0PQhSdq9e3fEsqCfoRS8RKIUPD3p2LFjgXWD9me0pRKDXve+++4r8/lDhw7ptttuC2wXwNmJETIAAB4gkAEA8ACBDACABwhkAAA8QCADAOABAhkAAA8QyAAAeKBC85CnTJkScW5vRkZGxHqNGzcObDdoab5o85CD5tVGm9+6Z8+ewPKkpKSIZdHmqAbNB442lzhoib5Dhw4F1g1aulEK3l/R9nVl3lNl6kb7/WnYsGHEsqA57tHabtu2bWDdK664IrC8LAUFBcxDBlAmRsgAAHiAQAYAwAMEMgAAHiCQAQDwAIEMAIAHCGQAADxQoWlPjRo1ijjtaeLEiVXSIQAAzkaMkAEA8ACBDACABwhkAAA8QCADAOABAhkAAA8QyAAAeIBABgDAAwQyAAAeIJABAPAAgQwAgAcIZAAAPFChe1kDAFDdxo0bV6n6kydPrqKeVC1GyAAAeIBABgDAAwQyAAAeIJABAPAAgQwAgAcIZAAAPEAgAwDgAQIZAAAPEMgAAHiAQAYAwAMEMgAAHiCQAQDwAIEMAIAHCGQAADxAIAMA4AECGQAADxDIAAB4gEAGAMADBDIAAB4gkAEA8ACBDACABwhkAAA8QCADAOABAhkAAA8QyAAAeIBABgDAAwQyAAAeIJABAPAAgQwAgAcIZAAAPEAgAwDgAQIZAAAPEMgAAHiAQAYAwAMEMgAAHiCQAQDwQM3q7gAA4My2bZuUnx97/ZQUKSOj6vrjKwIZAHDabNsmtW0rHT4cext16kgbNpz5ocwhawDAaZOfX7kwllz9yoywfyoIZAAAPEAgAwDgAQIZAAAPEMgAAHiAQAYAwAMEMgAAHiCQAQDwAIEMAIAHynWnLjOTJBUUFJzWzgBnutD/odD/KQAIKVcgFxYWSpLS09NPa2eAs0VhYaEaNGhQ3d0A4JFyBXJaWpq2b9+upKQkxcXFne4+AWcsM1NhYaHS0tKquysAPFOuQK5Ro4ZatWp1uvsCnBUYGQMoCxd1AQDgAQIZAAAPEMgAAHiAQAYAwAMEciQ33yxdfXX5t//6aykuTsrNPT39OR3++lepWTPXd99UdP+X1w03SA8+WPXtAkAl+R3IP/wg3X67lJEh1a4ttWgh9esnffRRdfcsduvWST//udSggVSvnnThhdK2beHySy91wX7i1223hcv37JEGDpTq15e6dJE++6x0+zk55Q+cCROkQYOk1q3Dz82dK/3937v+JSVJHTpIo0fH+GY99Nvfuve9f3919wQASinXtKdqc9110tGj0v/+r5SVJe3aJS1a5EZ2P0VbtkgXXSSNHCndd5+UnCx9+aVUp07p7X7xC+kPfwg/rls3/P2ECVJhobR6tTR1qtv2009d2fLl0ooV0p/+FL0vBw9Kzz4rLVgQfm7RImnIEPcaP/+5+zCwdq20cGHs79kXR49KCQlSx45Sdrb0wgvuwwsi4g59PyEHDlRdO1X88z4dXTty5Eil2vpb/06X+w595qu9e80ks8WLg7d78EGzjh3N6tY1a9XK7PbbzQoLw+XTp5s1aGD29ttm7dqZ1atn1q+f2Y4d4W2OHze78063XePGZmPHmt10k9mgQeFt5s8369MnvM2AAWabN4fLv/rK9fezzyL3dcgQs2HDgt/PJZeYjRoVubx/f7OpU933a9e6921mdvSoWefOZitXBrcf8uKLZk2bln5u1CizSy8Nrjd+vHudP//ZLDPTLDnZva+CgvA2xcVmEyeatW5tVqeOWadO7vVCjh83GzEiXH7eeWYPP1z6dYYPL73/P/nELCXFbPJk93jvXrORI91zSUlm//APZrm5p/bz6afd68TFhcvuu8/soouC3yds+/btJokvvviqoq/t27cH/p/zd4Rcv777evVVdwi1du2yt6tRw40I27SRtm6VfvUr6d//XXriifA2Bw9K//Vf0vPPu+2HDZPuukuaMcOVP/ig9Nxz0v/8j9S+vXs8d6502WXhNoqKpDFjpE6d3Ee1//gP6Zpr3DnjGuU48l9SIr35putbv37uUHObNtLdd596rnTGDDeCa9HCHZ7+3e/Co+TOnaX33pNuvdWNbjt1cs9PmeIOd3fvHr0vkvTBB1K3bqWfa9FCmjlTystzI8lItmxxP5d586S9e6Xrr5cmT3Yja0maNMn1/8knpXPPlZYudfu8aVPpkkvcvmjVSnrxRalJE+njj6Vf/lJKTXVtney996Rrr3Xv8Ze/dM8NHiwlJkrz57vD6089JfXtK23cKDVu7LbZvFl6+WXplVek+Phwez16uL4eORL59wpVcoe+goICpaena/v27UpOTq50n6qyPZ/7VtXt0bfqbc/Ke4e+v81n7Ri99JJZo0ZuFNW7t9ndd5t9/nlwnRdfNGvSJPx4+nQ3cj1xNPv442bNm4cfp6aaTZkSfnzsmBttnzhCO9kPP7h216xxj6ONkHfudOV165o99JDbbtIkN3I78SjAU0+50fwXX5i98IJZy5Zm11wTLt+3z+zGG80yMsx+9jOzL78027jR7NxzzfLzzf7lX8zatDEbPNhtG8mgQW6UeqIDB8z+8R9dPzMz3cj32WfNDh8ObzN+vHsPJ46Ix44169nTfX/4sCv/+OPSbY8c6fodSU6O2XXXhR+HRsivvGJWv77ZrFnhsg8+cCPzE/tlZpad7fZfqJ+1apnt3n3qa33+uXuPX38duT+oEvv37zdJtn//fu/a87lvVd0effOnvSD+jpAldw55wAA3mlu+3I2GpkyRnnnGXYUrSe++60Zk69e7EwzHj0uHD7tRcWhUWbeuO28Ykpoq7d7tvt+/X9q5U+rZM1xes6YbaZ54vH/TJjcqXrFCys93ozzJXZAVNJoMCW0/aJB0553u+7/7Ozc6fPJJN3KUwiNASbrgAtfXvn3dqDQ7240GZ84s3fZll0n/+Z9uZL11q7RhQ/g8dKQLvA4dOvXcdb16bhS/ZYv0/vtun//bv0mPPCItWxben61buwu+Qk7cn5s3u31/xRWl2z561F2EFvL44+6IxLZtri9Hj7r9caIVK9wo/KWXSh9F+Pxzd5SiSZNT39OWLeHHmZluVH6yxET378GDZewYAKgefl9lLbnQuOIKd9j2449dEI8f78q+/lr6p39yh21ffllatcr9oZfcH/iQWrVKtxkXVzpsy2PgQHeF89NPu6BYseLU1wmSkuKC/vzzSz/fvn3pq6xPFvqgsHlz2eXTp0sNG7qgX7zYBVetWu6Q7uLFwf3Zu7fssuxsd0j8mWfcxWNr10qzZ4fLy9qfoQ8coSs43nzTHc4Pfa1d64JVkmbNcqcMRo6U3nnHld9yy6n7MjtbatfOBfexY+HnDxxwHwJObD83130QGTs2vF29emW/vz173L9lhTUAVBO/R8hlOf98d/5ScgFcUuJGgaHzuHPmVKy9Bg3cH/cVK6Sf/cw9d/y4a7trV/f4r391f+yfflq6+GL33IcfVux1EhLcFKcNG0o/v3GjG8lFEprXnJp6atkPP7hRcKgvxcXh4Dp2zD2OpEsXd543mtat3ci4qCj6tpL7+dSu7T5khEb9J/voI6l3b3e+P+TEkW1ISoo7/3vppe7c8pw57sNA167S99+7DzgnTtkqr7w8dw47JaXidVEhtWvX1vjx41W7is7VV2V7Pvetqtujb/60F+i0HxSPVX6+u3L2+efdOb+tW83mzHHnfkPnPnNz3bnAhx8227LFXfnbsqV7bu9et03oKusTzZ3rtgmZPNldOT13rtm6dWa/+IW7cjd0Drm42J2XHjbMbNMms0WLzC680LUxd67bpjxXWb/yijuvOW2aa+fRR83i4905UTN3nvsPfzD79FPX3muvmWVluXPFZfnnf3ZthDzwgFm3bu7q6/79zX71q8h9+eILs5o1zfbsCT83frw7H/z++25/r15tdvPNZomJZuvXh7fp3Ll0W//93+6cc8i997r99dxz7j2tWmX2pz+5x2ZmjzzizgG//bbZhg1mv/2te3xiuydeZb1zp7tC/rrr3Pn9khJ3lXTnzmYLFrh99dFHZvfcE77KvKx+ntj2yefPAaCa+RvIhw+bjRtn1rWrC9S6dc3atnV/vA8eDG/30EPuoqzERDed6c9/rnggHzvmpvwkJ5s1bGg2Zsyp054WLjRr396sdm03jWfx4ooHspm7SOqcc9yFap07m736arhs2zYXvo0bu9c55xwXkGVdTPD222Y9ergPCyFFRe5irqQks759zXbtCu5Ljx5mTz4Zfvzeey700tPNEhLch5+rrgp/YDArXyCXlLgPSW3bug8gTZu6n82SJa788GEX9A0auP19++3uZx0pkM3cNLXzzjO7/no3baqgwOxf/9UsLc29Rnq62dChbh9G6qeZ2aFD7nWXLQveNwDwNxZnVtGTqThjvPmmO+eal1e+qVtngqlT3ZS2d96p7p4AQCk/vXPIqDoDBrirx7/7TkpPr+7e/G3UqiU9+mh19wIATsEIGQAAD5wlxykBAPAbgQzgtHn88cfVunVr1alTRz179tQnn3wSUztLly7VwIEDlZaWpri4OL0amvoYg0mTJunCCy9UUlKSmjVrpquvvlobTp6OWAFTp05Vp06dlJycrOTkZPXq1Uvz58+Pub0TTZ48WXFxcRod44prv//97xUXF1fqq127djH357vvvtOwYcPUpEkTJSYm6oILLtCnocVtKqh169an9C0uLk45MS76UlxcrN/97ndq06aNEhMTlZ2drfvvvz/6gg4RFBYWavTo0crMzFRiYqJ69+6tlStXxtRWeRHIAE6L2bNna8yYMRo/frxWr16tzp07q1+/ftoduqtbBRQVFalz5856PHTjn0pYsmSJcnJytHz5ci1cuFDHjh3TlVdeqaLyzrU/SatWrTR58mStWrVKn376qS677DINGjRIX375ZaX6uXLlSj311FPqFLpffYw6dOignTt3/vj1YUXvofD/9u7dqz59+qhWrVqaP3++1q5dqwcffFCNGjWKqb2VK1eW6tfC/19VbvDgwTG198ADD2jq1Kl67LHHtG7dOj3wwAOaMmWKHo3xmpFbb71VCxcu1PPPP681a9boyiuv1OWXX67vvvsupvbKpVqv8QZwxurRo4fl5OT8+Li4uNjS0tJs0qRJlWpXks0NTTesArt37zZJtiQ0La8KNGrUyJ555pmY6xcWFtq5555rCxcutEsuucRGBa0AF2D8+PHWOdJ8/Ar6zW9+YxedxlXSRo0aZdnZ2VZSUhJT/QEDBtiIk+4vcO2119rQoUMr3NbBgwctPj7e5s2bV+r5rl272r333htT/8qDETKAKnf06FGtWrVKl19++Y/P1ahRQ5dffrmWLVtWjT071f79+yVJjUOrhFVCcXGxZs2apaKiIvXq1SvmdnJycjRgwIBS+y9WmzZtUlpamrKysjR06FBtC7pVb4DXX39d3bt31+DBg9WsWTN16dJFTz/9dKX7J7nflxdeeEEjRoyIeWWx3r17a9GiRdq4caMk6fPPP9eHH36o/v37V7it48ePq7i4WHVOut9/YmJizEcYyoNpTwCqXH5+voqLi9W8efNSzzdv3lzr16+vpl6dqqSkRKNHj1afPn3UsTyLxESwZs0a9erVS4cPH1b9+vU1d+5cnX/yfevLadasWVq9enWVnK/s2bOnnnvuObVt21Y7d+7Ufffdp4svvlh5eXlKOnGBmHLYunWrpk6dqjFjxuiee+7RypUr9etf/1oJCQkaPnx4pfr56quvat++fbo5tGhQDMaNG6eCggK1a9dO8fHxKi4u1oQJEzR06NAKt5WUlKRevXrp/vvvV/v27dW8eXP95S9/0bJly3TOOefE3MdoCGQAZ62cnBzl5eVVetTTtm1b5ebmav/+/XrppZc0fPhwLVmypMKhvH37do0aNUoLFy48ZXQWixNHh506dVLPnj2VmZmpOXPmaOTIkRVqq6SkRN27d9fEiRMlSV26dFFeXp6efPLJSgfys88+q/79+0dfLzjAnDlzNGPGDM2cOVMdOnRQbm6uRo8erbS0tJj69/zzz2vEiBFq2bKl4uPj1bVrV914441atWpVzH2MhkAGUOVSUlIUHx+vXbt2lXp+165datGiRTX1qrQ77rhD8+bN09KlS9WqVatKtZWQkPDjyKlbt25auXKlHnnkET311FMVamfVqlXavXu3uoYWtpE7DL506VI99thjOnLkiOLj42PuZ8OGDXXeeedpc6TV4wKkpqae8gGjffv2evnll2PujyR98803evfdd/XKK69Uqp2xY8dq3LhxuuGGGyRJF1xwgb755htNmjQppkDOzs7WkiVLVFRUpIKCAqWmpmrIkCHKysqqVD+DcA4ZQJVLSEhQt27dtGjRoh+fKykp0aJFiyp1brUqmJnuuOMOzZ07V++9957atGlT5a9RUlKiI0eOVLhe3759tWbNGuXm5v741b17dw0dOlS5ubmVCmNJOnDggLZs2aLUslaPi6JPnz6nTA/buHGjMoNWqyuH6dOnq1mzZhowYECl2jl48KBqnHQL4Pj4eJWEloaNUb169ZSamqq9e/dqwYIFGjRoUKXaC8IIGcBpMWbMGA0fPlzdu3dXjx499PDDD6uoqEi33HJLhds6cOBAqVHdV199pdzcXDVu3FgZGRkVaisnJ0czZ87Ua6+9pqSkJH3//feSpAYNGigxMbHCfbv77rvVv39/ZWRkqLCwUDNnztTixYu1YMGCCreVlJR0yrnsevXqqUmTJjGd477rrrs0cOBAZWZmaseOHRo/frzi4+N14403VritO++8U71799bEiRN1/fXX65NPPtG0adM0bdq0CrcVUlJSounTp2v48OGqWbNycTRw4EBNmDBBGRkZ6tChgz777DM99NBDGjFiREztLViwQGamtm3bavPmzRo7dqzatWsX0+9vuZ2267cBnPUeffRRy8jIsISEBOvRo4ctX748pnbef/99k3TK1/DhwyvcVlntSLLp06fH1LcRI0ZYZmamJSQkWNOmTa1v3772zjvvxNRWWSoz7WnIkCGWmppqCQkJ1rJlSxsyZIht3rw55r688cYb1rFjR6tdu7a1a9fOpk2bFnNbZmYLFiwwSbZhw4ZKtWNmVlBQYKNGjbKMjAyrU6eOZWVl2b333mtHjhyJqb3Zs2dbVlaWJSQkWIsWLSwnJ8f27dtX6X4G4V7WAAB4gHPIAAB4gEAGAMADBDIAAB4gkAEA8ACBDACABwhkAAA8QCADAOABAhkAAA8QyAAAeIBABgDAAwQyAAAe+D+j9bIuqNtHXQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 12\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kgdvGD52CaXR"
   },
   "source": [
    "让我们用模型的预测绘制几张图像。请注意，即使置信度很高，模型也可能出错。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:50.280404Z",
     "iopub.status.busy": "2023-11-08T00:32:50.279613Z",
     "iopub.status.idle": "2023-11-08T00:32:52.201082Z",
     "shell.execute_reply": "2023-11-08T00:32:52.200200Z"
    },
    "id": "hQlnbqaw2Qu_"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first X test images, their predicted labels, and the true labels.\n",
    "# Color correct predictions in blue and incorrect predictions in red.\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, predictions[i], test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, predictions[i], test_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R32zteKHCaXT"
   },
   "source": [
    "## 使用训练好的模型\n",
    "\n",
    "最后，使用训练好的模型对单个图像进行预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:52.205791Z",
     "iopub.status.busy": "2023-11-08T00:32:52.205507Z",
     "iopub.status.idle": "2023-11-08T00:32:52.209770Z",
     "shell.execute_reply": "2023-11-08T00:32:52.209071Z"
    },
    "id": "yRJ7JU7JCaXT"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Grab an image from the test dataset.\n",
    "img = test_images[1]\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vz3bVp21CaXV"
   },
   "source": [
    "`tf.keras` 模型经过了优化，可同时对一个*批*或一组样本进行预测。因此，即便您只使用一个图像，您也需要将其添加到列表中："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:52.213041Z",
     "iopub.status.busy": "2023-11-08T00:32:52.212781Z",
     "iopub.status.idle": "2023-11-08T00:32:52.216838Z",
     "shell.execute_reply": "2023-11-08T00:32:52.216138Z"
    },
    "id": "lDFh5yF_CaXW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Add the image to a batch where it's the only member.\n",
    "img = (np.expand_dims(img,0))\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EQ5wLTkcCaXY"
   },
   "source": [
    "现在预测这个图像的正确标签："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:52.220259Z",
     "iopub.status.busy": "2023-11-08T00:32:52.219645Z",
     "iopub.status.idle": "2023-11-08T00:32:52.316966Z",
     "shell.execute_reply": "2023-11-08T00:32:52.316168Z"
    },
    "id": "o_rzNSdrCaXY"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "1/1 [==============================] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1/1 [==============================] - 0s 48ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.8946848e-05 8.3171351e-16 9.9309057e-01 8.6931016e-13 5.6834291e-03\n",
      "  8.8734559e-13 1.2070854e-03 1.5665156e-16 1.2896362e-11 6.2792527e-16]]\n"
     ]
    }
   ],
   "source": [
    "predictions_single = probability_model.predict(img)\n",
    "\n",
    "print(predictions_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:52.320682Z",
     "iopub.status.busy": "2023-11-08T00:32:52.319945Z",
     "iopub.status.idle": "2023-11-08T00:32:52.411625Z",
     "shell.execute_reply": "2023-11-08T00:32:52.410885Z"
    },
    "id": "6Ai-cpLjO-3A"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_value_array(1, predictions_single[0], test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cU1Y2OAMCaXb"
   },
   "source": [
    "`keras.Model.predict` 会返回一组列表，每个列表对应一批数据中的每个图像。在批次中获取对我们（唯一）图像的预测："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T00:32:52.415511Z",
     "iopub.status.busy": "2023-11-08T00:32:52.414928Z",
     "iopub.status.idle": "2023-11-08T00:32:52.419907Z",
     "shell.execute_reply": "2023-11-08T00:32:52.419239Z"
    },
    "id": "2tRmdq_8CaXb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions_single[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YFc2HbEVCaXd"
   },
   "source": [
    "该模型会按照预期预测标签。"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "classification.ipynb",
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
